Build your first AppSheet app: how I built a food tracker

Posted by Filipe Gracio, PhD – Customer Engineer

I keep forgetting what I have in the freezer. At first I used Google Sheets to keep track of it, but I wanted something that was easy to consult and update on my smartphone. So I turned to AppSheet! Here’s a tutorial to follow to make a similar tracking solution.

Creating the database

First I created a database that imported my data from the Sheet:

A cropped screen shot illustrating creating a database in AppSheet by importing data from sheets

After I selected “Import from Sheets” and selected the sheet I was cumbersomely maintaining, I get the preview of the new database:

A cropped screen shot illustrating creating a database in AppSheet by importing data from sheets

Creating the App

Then I can go back and create an App:

A cropped screen shot illustrating creating an app

After I name it I can select the database I just created.

A cropped screen shot illustrating step 1 of selecting the database

Then

A cropped screen shot illustrating step 2 of selecting the database

The App now starts getting created, and then I can start customizing it!

Customizing the App:

I decided I want to actually add more information to the App. For example, I want to categorize my items, so I need another column. I can edit the data for this and I’ll add a column “Category”.

A cropped screen shot illustrating editing the data

After adding the extra column, this is the result:

A cropped screen shot showing the data with the new column added

That’s going to come in handy later for presentation and organization!

Now let’s do some configuration about how the items are presented on the actual app. That’s in the UX section of the App builder. I want to select “Table”, Group by “category” and then sort alphabetically by “Item”

A cropped screen shot showing The Pirmary views in the UX Section of the App builder

After tweaking a few more options in UX “Brand” and “Format Rules”, this is how my app is looks:

A screen shot of the app on a mobile device displaying with content from the original dataset

Using the App – adding and updating items.

Now, I can see what I have in the freezer at all times. If I cook something and have a leftover, I can just add it by clicking the + button. After that, I just need to add in the info:

A screen shot illustrating the functionality of the app on a mobile device

And of course, if I use something I can just tap on it to edit the amount (or delete it).

Try it yourself!

This small App is something I use every week now! It is much easier than my old method, plus I learned how to use AppSheet. And this was just a quite simple use case – which only touched the tip of the iceberg of AppSheet’s features. If you work for organizations that have information to share and organize, this technology could be useful for you.

Try it out for yourself: you can use the complete set of AppSheet features at no cost while building one or many app prototypes. You can also invite up to 10 test users at no cost to use your apps and share feedback.

Thank you to my colleague Florian Opitz, Customer Engineer – Google Workspace + Security , for his useful edits and suggestions.

ML Olympiad 2023: Globally Distributed ML Competitions by Google ML Community

Posted by Hee Jung, DevRel Community Manager

What is the ML Olympiad?

The ML Olympiad is an associated Kaggle Community Competitions hosted by ML GDE, TFUG, 3rd-party ML communities, supported by Google Developers. The ML Developer Programs team and the communities successfully ran the first round of the campaign in 2022 and are now launching the second round. The goal of this campaign is to provide ML training opportunities for developers by leveraging Kaggle’s features.

ML Olympiad Community Competitions

17 ML Olympiad community competitions are currently open. Visit the ML Olympiad page to participate.

Into the Space

  • Predicting which spaceship passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.
  • Host: MD Shahriyar Al Mustakim Mitul / TFUG Dhaka

    Water Quality Prediction

    • Estimating the quality of water.
    • Hosts: Usha Rengaraju, Vijayabharathi Karuppasamy (TFUG Chennai), Samuel T (TFUG Mysuru)

      Breast Cancer Diagnosis

      • Predicting medical diagnosis [breast cancer].
      • Host: Ankit Kumar Verma / TFUG Prayagraj

        Book Recommendations

        • To provide personalized recommendations to users based on their reading history and preferences using various machine learning algorithms.
        • Hosts: Anushka Raj, Yugandhar Surya / TFUG Hajipur

          Argania Tree Deforestation Detection

          • Use Sentinel-2 satellite imagery to detect and map areas of deforestation in the Argania region.
          • Hosts: Taha Bouhsine / TFUG Agadir

            Multilingual Spell Correction

            • Reconstruct noisy sentences in European languages: English, French, German, Bulgarian and Turkish.
            • Host: Radostin Cholakov (ML GDE)

              CO2 Emissions Forecasting

              • Forecasting CO2 emissions based on deforestation in Côte d’Ivoire.
              • Hosts: Armel Yara, Kimana Misago, Jordan Erifried / TFUG Abidjan

                Ensure Healthy Lives (in local language) 

                • Use ML techniques to help achieve common good health and well-being.
                • Hosts: Vinicius Fernandes Caridá (ML GDE), Pedro Gengo, Alex Fernandes Mansano / TFUG São Paulo

                  Predictive Maintenance

                  • Predict future engine’s failures.
                  • Host: Daniel Pereda / TFUG Santiago

                    Firetrucks Are Red And Cars Are Blue

                    • To create a model that can accurately predict the correct class for each image, without overfitting.
                    • Host: Prasoon Kottarathil / TFUG Thrissur

                      Dialect Recognition (in Arabic) 

                      • Dialect recognition in order to improve user experience in AI applications.
                      • Hosts: Ruqiya Bin Safi (ML GDE), Eyad Sibai, Hussain Alfayez / Saudi TFUG & Applied ML/AI group

                        Sentiment Analysis Of JUMIA Tunisia  (in local language) 

                        • Use JUMIA customer reviews to determine the sentiment of content from text data.
                        • Host: Boulbaba BEN AMMAR / TFUG Sfax

                          Kolkata Housing Prediction

                          • Kolkata housing prediction results can be used to address related social and economic issues.
                          • Host: Rishiraj Acharya / TFUG Kolkata

                            Can You Guess The Beer Style?

                            • This is a machine learning competition focused on classifying beer into 17 distinct styles based on key descriptors.
                            • Host: Marvik

                              Detect ChatGpt answers

                              • The goal of this competition is to classify ChatGpt answers vs real human answers for a variety of questions.
                              • Host: Elyes Manai (ML GDE) / IEEE ESSTHS + GDSC ISETSO + PyData Tunisia

                                MLAct Pose Detection

                                • Raising awareness about some basic yoga poses, and encouraging our community members to practice the basic parts of computer vision.
                                • Host: Imen Masmoudi / MLAct ML Community

                                  Hausa Sentiment Analysis 2.0 (in local language) 

                                  • Classify the sentiment of sentences of Hausa Language.
                                  • Hosts: Nuruddeen Sambo, Dattijo Murtala Makama / TFUG Bauchi

                                    Navigating ML Olympiad

                                    You can search “ML Olympiad” on Kaggle Community Competitions page to see them all. And for further info, look for #MLOlympiad on social media.

                                    Google Developers supports the hosts of each competition. Browse through the available competitions and participate in those that interest you!

                                    Machine Learning Communities: Q4 ‘22 highlights and achievements

                                    Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

                                    Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the last quarter of 2022. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

                                    ML at DevFest 2022

                                    A group of ML Developers attending DevFest 2022
                                    A large number of members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide covering various ML topics with Google products. Machine Learning with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Easy ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted great sessions.

                                    ML Community Summit 2022

                                    A group of ML Developers attending ML Community Summit
                                    ML Community Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most active community members (ML GDE or TFUG organizer) were invited and shared their past activities and thoughts on Google’s ML products. A video sketch from ML Developer Programs team and a blog posting by ML GDE Margaret Maynard-Reid (United States) help us revisit the moments.

                                    TensorFlow

                                    MAXIM in TensorFlow by ML GDE Sayak Paul (India) shows his implementation of the MAXIM family of models in TensorFlow.

                                    Diagram of gMLP block

                                    gMLP: What it is and how to use it in practice with Tensorflow and Keras? by ML GDE Radostin Cholakov (Bulgaria) demonstrates the state-of-the-art results on NLP and computer vision tasks using a lot less trainable parameters than corresponding Transformer models. He also wrote Differentiable discrete sampling in TensorFlow.

                                    Building Computer Vision Model using TensorFlow: Part 2 by TFUG Pune for the developers who want to deep dive into training an object detection model on Google Colab, inspecting the TF Lite model, and deploying the model on an Android application. ML GDE Nitin Tiwari (India) covered detailed aspects for end-to-end training and deployment of object model detection.

                                    Advent of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) solving the Advent of Code (AoC) puzzles using only TensorFlow. The articles contain a description of the solutions of the Advent of Code puzzles 1-5, in pure TensorFlow.

                                    tf.keras.metrics / tf.keras.optimizers by TFUG Taipei helped people learn the TF libraries. They shared basic concepts and how to use them using Colab.
                                    Screen shot of TensorFlow Lite on Android Project Practical Course
                                    A hands-on course on TensorFlow Lite projects on Android by ML GDE Xiaoxing Wang (China) is the book mainly introducing the application of TensorFlow Lite in Android development. The content focuses on applying three typical ML applications in Android development.

                                    Build tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files with Colab by ML GDE George Soloupis (Greece) guides how you can shrink the final size of your Android application’s .apk by building tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files without the need of Docker or personal PC environment.

                                    TensorFlow Lite and MediaPipe Application by ML GDE XuHua Hu (China) explains how to use TFLite to deploy an ML model into an application on devices. He shared experiences with developing a motion sensing game with MediaPipe, and how to solve problems that we may meet usually.

                                    Train and Deploy TensorFlow models in Go by ML GDE Paolo Galeone (Italy) delivered the basics of the TensorFlow Go bindings, the limitations, and how the tfgo library simplifies their usage.

                                    Keras

                                    Diagram of feature maps concatenated together and flattened
                                    Complete Guide on Deep Learning Architectures, Chapter 1 on ConvNets by ML GDE Merve Noyan (France) brings you into the theory of ConvNets and shows how it works with Keras.
                                    Hazy Image Restoration Using Keras by ML GDE Soumik Rakshit (India) provides an introduction to building an image restoration model using TensorFlow, Keras, and Weights & Biases. He also shared an article Improving Generative Images with Instructions: Prompt-to-Prompt Image Editing with Cross Attention Control.
                                    Mixed precision in Keras based Stable Diffusion
                                    Let’s Generate Images with Keras based Stable Diffusion by ML GDE Chansung Park (Korea) delivered how to generate images with given text and what stable diffusion is. He also talked about Keras-based stable diffusion, basic building blocks, and the advantages of using Keras-based stable diffusion.
                                    A Deep Dive into Transformers with TensorFlow and Keras: Part 1, Part 2, Part3 by ML GDE Aritra Roy Gosthipaty (India) covered the journey from the intuition of attention to formulating the multi-head self-attention. And TensorFlow port of GroupViT in 🤗 transformers library was his contribution to Hugging Face transformers library.

                                    TFX

                                    Digits + TFX banner
                                    How startups can benefit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from applying TFX early, how TFX helps Digits grow, and how other startups can benefit from TFX too.

                                    Usha Rengaraju (India) shared TensorFlow Extended (TFX) Tutorials (Part 1, Part 2, Part 3) and the following TF projects: TensorFlow Decision Forests Tutorial and FT Transformer TensorFlow Implementation.

                                    Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) explained hyperparam tuning, why it is important; Introduction to KerasTuner, basic usage; how to visualize hyperparam tuning results with TensorBoard; and integration within ML pipeline with TFX.

                                    JAX/Flax

                                    JAX High-performance ML Research by TFUG Taipei and ML GDE Jerry Wu (Taiwan) introduced JAX and how to start using JAX to solve machine learning problems.

                                    [TensorFlow + TPU] GatedTabTransformer[W&B] and its JAX/Flax counterpart GatedTabTransformer-FLAX[W&B] by Usha Rengaraju (India) are tutorial series containing the implementation of GatedTabTransformer paper in both TensorFlow (TPU) and FLAX.
                                    Putting NeRF on a diet: Semantically consistent Few-Shot View Synthesis Implementation
                                    JAX implementation of Diet NeRf by ML GDE Wan Hong Lau (Singapore) implemented the paper “Putting NeRF on a Diet (DietNeRF)” in JAX/Flax. And he also implemented a JAX-and-Flax training pipeline with the ResNet model in his Kaggle notebook, 🐳HappyWhale🔥Flax/JAX⚡TPU&GPU – ResNet Baseline.
                                    Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the basics of the requirements we have on a DL framework to what JAX has to offer. Further, he focused on the powerful function-oriented view JAX offers and how Flax allows you to use them in training neural networks.
                                    Screen grab of ML GDE David Cardozo and Cristian Garcia during a live coding session of a review of new features, specifically Shared Arrays, in the recent release of JAX
                                    JAX Streams: Exploring JAX 0.4 by ML GDE David Cardozo (Canada) and Cristian Garcia (Colombia) showed a review of new features (specifically Shared Arrays) in the recent release of JAX and demonstrated live coding.
                                    [LiveCoding] Train ResNet/MNIST with JAX/Flax by ML GDE Qinghua Duan (China) demonstrated how to train ResNet using JAX by writing code online.

                                    Kaggle

                                    Low-light Image Enhancement using MirNetv2 by ML GDE Soumik Rakshit (India) demonstrated the task of Low-light Image Enhancement.

                                    Heart disease Prediction and Diabetes Prediction Competition hosted by TFUG Chandigarh were to familiarize participants with ML problems and find solutions using classification techniques.
                                    TensorFlow User Group Bangalore Sentiment Analysis Kaggle Competition 1
                                    TFUG Bangalore Kaggle Competition – Sentiment Analysis hosted by TFUG Bangalore was to find the best sentiment analysis algorithm. Participants were given a set of training data and asked to submit an ML/DL algorithm that could predict the sentiment of a text. The group also hosted Kaggle Challenge Finale + Vertex AI Session to support the participants and guide them in learning how to use Vertex AI in a workflow.

                                    Cloud AI

                                    Better Hardware Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) discussed the pain points of provisioning hardware (especially for ML experiments) and how we can get better provision hardware with code using Vertex AI Workbench instances and Terraform.

                                    Jayesh Sharma, Platform Engineer, Zen ML; MLOps workshop with TensorFlow and Vertex AI November 12, 2022|TensorFlow User Group Chennai
                                    MLOps workshop with TensorFlow and Vertex AI by TFUG Chennai targeted beginners and intermediate-level practitioners to give hands-on experience on the E2E MLOps pipeline with GCP. In the workshop, they shared the various stages of an ML pipeline, the top tools to build a solution, and how to design a workflow using an open-source framework like ZenML.
                                    10 Predictions on the Future of Cloud Computing by 2025: Insights from Google Next Conference by ML GDE Victor Dibia (United States) includes a recap of his notes reflecting on the top 10 cloud technology predictions discussed at the Google Cloud Next 2022 keynote.
                                    Workflow of Google Virtual Career Center
                                    O uso do Vertex AI Matching Engine no Virtual Career Center (VCC) do Google Cloud by ML GDE Rubens Zimbres (Brazil) approaches the use of Vertex AI Matching Engine as part of the Google Cloud Virtual Career Center solution.

                                    More practical time-series model with BQML by ML GDE JeongMin Kwon (Korea) introduced BQML and time-series modeling and showed some practical applications with BQML ARIMA+ and Python implementations.

                                    Vertex AI Forecast – Demand Forecasting with AutoML by ML GDE Rio Kurihara (Japan) presented a time series forecast overview, time series fusion transformers, and the benefits and desired features of AutoML.

                                    Research & Ecosystem

                                    AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) introduced AI applications in healthcare and the challenges facing AI in its adoption into the health system.

                                    Women in AI APAC finished their journey at ML Paper Reading Club. During 10 weeks, participants gained knowledge on outstanding machine learning research, learned the latest techniques, and understood the notion of “ML research” among ML engineers. See their session here.

                                    A Natural Language Understanding Model LaMDA for Dialogue Applications by ML GDE Jerry Wu (Taiwan) introduced the natural language understanding (NLU) concept and shared the operation mode of LaMDA, model fine-tuning, and measurement indicators.

                                    Python library for Arabic NLP preprocessing (Ruqia) by ML GDE Ruqiya Bin (Saudi Arabia) is her first python library to serve Arabic NLP.

                                    Screengrab of ML GDEs Margaret Maynard-Reid and Akash Nain during Chat with ML GDE Akash
                                    Chat with ML GDE Vikram & Chat with ML GDE Aakash by ML GDE Margaret Maynard-Reid (United States) shared the stories of ML GDEs’ including how they became ML GDE and how they proceeded with their ML projects.

                                    Anatomy of Capstone ML Projects 🫀by ML GDE Sayak Paul (India) discussed working on capstone ML projects that will stay with you throughout your career. He covered various topics ranging from problem selection to tightening up the technical gotchas to presentation. And in Improving as an ML Practitioner he shared his learning from experience in the field working on several aspects.

                                    Screen grab of statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
                                    MLOps Development Environment by ML GDE Vinicius Caridá (Brazil) aims to build a full development environment where you can write your own pipelines connecting MLFLow, Airflow, GCP and Streamlit, and build amazing MLOps pipelines to practice your skills.

                                    Transcending Scaling Laws with 0.1% Extra Compute by ML GDE Grigory Sapunov (UK) reviewed a recent Google article on UL2R. And his posting Discovering faster matrix multiplication algorithms with reinforcement learning explained how AlphaTensor works and why it is important.

                                    Back in Person – Prompting, Instructions and the Future of Large Language Models by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This event covered recent advances in the field of large language models (LLMs).

                                    ML for Production: The art of MLOps in TensorFlow Ecosystem with GDG Casablanca by TFUG Agadir discussed the motivation behind using MLOps and how it can help organizations automate a lot of pain points in the ML production process. It also covered the tools used in the TensorFlow ecosystem.

                                    Machine Learning Communities: Q3 ‘22 highlights and achievements

                                    Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

                                    Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the third quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

                                    TensorFlow/Keras

                                    Load-testing TensorFlow Serving’s REST Interface

                                    Load-testing TensorFlow Serving’s REST Interface by ML GDE Sayak Paul (India) and Chansung Park (Korea) shares the lessons and findings they learned from conducting load tests for an image classification model across numerous deployment configurations.

                                    TFUG Taipei hosted events (Python + Hugging Face-Translation+ tf.keras.losses, Python + Object detection, Python+Hugging Face-Token Classification+tf.keras.initializers) in September and helped community members learn how to use TF and Hugging face to implement machine learning model to solve problems.

                                    Neural Machine Translation with Bahdanau’s Attention Using TensorFlow and Keras and the related video by ML GDE Aritra Roy Gosthipaty (India) explains the mathematical intuition behind neural machine translation.

                                    Serving a TensorFlow image classification model as RESTful and gRPC based services with TFServing, Docker, and Kubernetes

                                    Automated Deployment of TensorFlow Models with TensorFlow Serving and GitHub Actions by ML GDE Chansung Park (Korea) and Sayak Paul (India) explains how to automate TensorFlow model serving on Kubernetes with TensorFlow Serving and GitHub Action.

                                    Deploying 🤗 ViT on Kubernetes with TF Serving by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to scale the deployment of a ViT model from 🤗 Transformers using Docker and Kubernetes.

                                    Screenshot of the TensorFlow Forum in the Chinese Language run by the tf.wiki team

                                    Long-term TensorFlow Guidance on tf.wiki Forum by ML GDE Xihan Li (China) provides TensorFlow guidance by answering the questions from Chinese developers on the forum.

                                    photo of a phone with the Hindi letter 'Ohm' drawn on the top half of the screen. Hinidi Character recognition shows the letter Ohm as the Predicted Result below.

                                    Hindi Character Recognition on Android using TensorFlow Lite by ML GDE Nitin Tiwari (India) shares an end-to-end tutorial on training a custom computer vision model to recognize Hindi characters. In TFUG Pune event, he also gave a presentation titled Building Computer Vision Model using TensorFlow: Part 1.

                                    Using TFlite Model Maker to Complete a Custom Audio Classification App by ML GDE Xiaoxing Wang (China) shows how to use TFLite Model Maker to build a custom audio classification model based on YAMNet and how to import and use the YAMNet-based custom models in Android projects.

                                    SoTA semantic segmentation in TF with 🤗 by ML GDE Sayak Paul (India) and Chansung Park (Korea). The SegFormer model was not available on TensorFlow.

                                    Text Augmentation in Keras NLP by ML GDE Xiaoquan Kong (China) explains what text augmentation is and how the text augmentation feature in Keras NLP is designed.

                                    The largest vision model checkpoint (public) in TF (10 Billion params) through 🤗 transformers by ML GDE Sayak Paul (India) and Aritra Roy Gosthipaty (India). The underlying model is RegNet, known for its ability to scale.

                                    A simple TensorFlow implementation of a DCGAN to generate CryptoPunks

                                    CryptoGANs open-source repository by ML GDE Dimitre Oliveira (Brazil) shows simple model implementations following TensorFlow best practices that can be extended to more complex use-cases. It connects the usage of TensorFlow with other relevant frameworks, like HuggingFace, Gradio, and Streamlit, building an end-to-end solution.

                                    TFX

                                    TFX Machine Learning Pipeline from data injection in TFRecord to pushing out Vertex AI

                                    MLOps for Vision Models from 🤗 with TFX by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for a vision model (TensorFlow) from 🤗 Transformers using the TF ecosystem.

                                    First release of TFX Addons Package by ML GDE Hannes Hapke (United States). The package has been downloaded a few thousand times (source). Google and other developers maintain it through bi-weekly meetings. Google’s Open Source Peer Award has recognized the work.

                                    TFUG São Paulo hosted TFX T1 | E4 & TFX T1 | E5. And ML GDE Vinicius Caridá (Brazil) shared how to train a model in a TFX pipeline. The fifth episode talks about Pusher: publishing your models with TFX.

                                    Semantic Segmentation model within ML pipeline by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for semantic segmentation task with TFX and various GCP products such as Vertex Pipeline, Training, and Endpoints.

                                    JAX/Flax

                                    Screen shot of Tutorial 2 (JAX): Introduction to JAX+Flax with GitHub Repo and Codelab via university of Amseterdam

                                    JAX Tutorial by ML GDE Phillip Lippe (Netherlands) is meant to briefly introduce JAX, including writing and training neural networks with Flax.

                                    TFUG Malaysia hosted Introduction to JAX for Machine Learning (video) and Leong Lai Fong gave a talk. The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model.

                                    TFUG Taipei hosted Python+ JAX + Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab.

                                    Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.

                                    A comparison of the performance and overview of issues resulting from changing from NumPy to JAX

                                    Should I change from NumPy to JAX? by ML GDE Gad Benram (Portugal) compares the performance and overview of the issues that may result from changing from NumPy to JAX.

                                    Introduction to JAX: efficient and reproducible ML framework by ML GDE Seunghyun Lee (Korea) introduced JAX/Flax and their key features using practical examples. He explained the pure function and PRNG, which make JAX explicit and reproducible, and XLA and mapping functions which make JAX fast and easily parallelized.

                                    Data2Vec Style pre-training in JAX by ML GDE Vasudev Gupta (India) shares a tutorial for demonstrating how to pre-train Data2Vec using the Jax/Flax version of HuggingFace Transformers.

                                    Distributed Machine Learning with JAX by ML GDE David Cardozo (Canada) delivered what makes JAX different from TensorFlow.

                                    Image classification with JAX & Flax by ML GDE Derrick Mwiti (Kenya) explains how to build convolutional neural networks with JAX/Flax. And he wrote several articles about JAX/Flax: What is JAX?, How to load datasets in JAX with TensorFlow, Optimizers in JAX and Flax, Flax vs. TensorFlow, etc..

                                    Kaggle

                                    DDPMs – Part 1 by ML GDE Aakash Nain (India) and cait-tf by ML GDE Sayak Paul (India) were announced as Kaggle ML Research Spotlight Winners.

                                    Forward process in DDPMs from Timestep 0 to 100

                                    Fresher on Random Variables, All you need to know about Gaussian distribution, and A deep dive into DDPMs by ML GDE Aakash Nain (India) explain the fundamentals of diffusion models.

                                    In Grandmasters Journey on Kaggle + The Kaggle Book, ML GDE Luca Massaron (Italy) explained how Kaggle helps people in the data science industry and which skills you must focus on apart from the core technical skills.

                                    Cloud AI

                                    How Cohere is accelerating language model training with Google Cloud TPUs by ML GDE Joanna Yoo (Canada) explains what Cohere engineers have done to solve scaling challenges in large language models (LLMs).

                                    ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google

                                    In Using machine learning to transform finance with Google Cloud and Digits, ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google, about how Digits leverages Google Cloud’s machine learning tools to empower accountants and business owners with near-zero latency.

                                    A tour of Vertex AI by TFUG Chennai for ML, cloud, and DevOps engineers who are working in MLOps. This session was about the introduction of Vertex AI, handling datasets and models in Vertex AI, deployment & prediction, and MLOps.

                                    TFUG Abidjan hosted two events with GDG Cloud Abidjan for students and professional developers who want to prepare for a Google Cloud certification: Introduction session to certifications and Q&A, Certification Study Group.

                                    Flow chart showing shows how to deploy a ViT B/16 model on Vertex AI

                                    Deploying 🤗 ViT on Vertex AI by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to deploy a ViT B/16 model on Vertex AI. They cover some critical aspects of a deployment such as auto-scaling, authentication, endpoint consumption, and load-testing.

                                    Photo collage of AI generated images

                                    TFUG Singapore hosted The World of Diffusion – DALL-E 2, IMAGEN & Stable Diffusion. ML GDE Martin Andrews (Singapore) and Sam Witteveen (Singapore) gave talks named “How Diffusion Works” and “Investigating Prompt Engineering on Diffusion Models” to bring people up-to-date with what has been going on in the world of image generation.

                                    ML GDE Martin Andrews (Singapore) have done three projects: GCP VM with Nvidia set-up and Convenience Scripts, Containers within a GCP host server, with Nvidia pass-through, Installing MineRL using Containers – with linked code.

                                    Jupyter Services on Google Cloud by ML GDE Gad Benram (Portugal) explains the differences between Vertex AI Workbench, Colab, and Deep Learning VMs.

                                    Google Cloud's Two Towers Recommender and TensorFlow

                                    Train and Deploy Google Cloud’s Two Towers Recommender by ML GDE Rubens de Almeida Zimbres (Brazil) explains how to implement the model and deploy it in Vertex AI.

                                    Research & Ecosystem

                                    WOMEN DATA SCIENCE, LA PAZ Club de lectura de papers de Machine Learning Read, Learn and Share the knowledge #MLPaperReadingClubs, Nathaly Alarcón, @WIDS_LaPaz #MLPaperReadingClubs

                                    The first session of #MLPaperReadingClubs (video) by ML GDE Nathaly Alarcon Torrico (Bolivia) and Women in Data Science La Paz. Nathaly led the session, and the community members participated in reading the ML paper “Zero-shot learning through cross-modal transfer.”

                                    In #MLPaperReadingClubs (video) by TFUG Lesotho, Arnold Raphael volunteered to lead the first session “Zero-shot learning through cross-modal transfer.”

                                    Screenshot of a screenshare of Zero-shot learning through cross-modal transfer to 7 participants in a virtual call

                                    ML Paper Reading Clubs #1: Zero Shot Learning Paper (video) by TFUG Agadir introduced a model that can recognize objects in images even if no training data is available for the objects. TFUG Agadir prepared this event to make people interested in machine learning research and provide them with a broader vision of differentiating good contributions from great ones.

                                    Opening of the Machine Learning Paper Reading Club (video) by TFUG Dhaka introduced ML Paper Reading Club and the group’s plan.

                                    EDA on SpaceX Falcon 9 launches dataset (Kaggle) (video) by TFUG Mysuru & TFUG Chandigarh organizer Aashi Dutt (presenter) walked through exploratory data analysis on SpaceX Falcon 9 launches dataset from Kaggle.

                                    Screenshot of ML GDE Qinghua Duan (China) showing how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

                                    Introduction to MRC-style dialogue summaries based on BERT by ML GDE Qinghua Duan (China) shows how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

                                    Plant disease classification using Deep learning model by ML GDE Yannick Serge Obam Akou (Cameroon) talked on plant disease classification using deep learning model : an end to end Android app (open source project) that diagnoses plant diseases.

                                    TensorFlow/Keras implementation of Nystromformer

                                    Nystromformer Github repository by Rishit Dagli provides TensorFlow/Keras implementation of Nystromformer, a transformer variant that uses the Nyström method to approximate standard self-attention with O(n) complexity which allows for better scalability.

                                    Machine Learning Communities: Q2 ‘22 highlights and achievements

                                    Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

                                    Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the second quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

                                    TensorFlow/Keras

                                    TFUG Agadir hosted #MLReady phase as a part of #30DaysOfML. #MLReady aimed to prepare the attendees with the knowledge required to understand the different types of problems which deep learning can solve, and helped attendees be prepared for the TensorFlow Certificate.

                                    TFUG Taipei hosted the basic Python and TensorFlow courses named From Python to TensorFlow. The aim of these events is to help everyone learn about the basics of Python and TensorFlow, including TensorFlow Hub, TensorFlow API. The event videos are shared every week via Youtube playlist.

                                    TFUG New York hosted Introduction to Neural Radiance Fields for TensorFlow users. The talk included Volume Rendering, 3D view synthesis, and links to a minimal implementation of NeRF using Keras and TensorFlow. In the event, ML GDE Aritra Roy Gosthipaty (India) had a talk focusing on breaking the concepts of the academic paper, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis into simpler and more ingestible snippets.

                                    TFUG Turkey, GDG Edirne and GDG Mersin organized a TensorFlow Bootcamp 22 and ML GDE M. Yusuf Sarıgöz (Turkey) participated as a speaker, TensorFlow Ecosystem: Get most out of auxiliary packages. Yusuf demonstrated the inner workings of TensorFlow, how variables, tensors and operations interact with each other, and how auxiliary packages are built upon this skeleton.

                                    TFUG Mumbai hosted the June Meetup and 110 folks gathered. ML GDE Sayak Paul (India) and TFUG mentor Darshan Despande shared knowledge through sessions. And ML workshops for beginners went on and participants built up machine learning models without writing a single line of code.

                                    ML GDE Hugo Zanini (Brazil) wrote Realtime SKU detection in the browser using TensorFlow.js. He shared a solution for a well-known problem in the consumer packaged goods (CPG) industry: real-time and offline SKU detection using TensorFlow.js.

                                    ML GDE Gad Benram (Portugal) wrote Can a couple TensorFlow lines reduce overfitting? He explained how just a few lines of code can generate data augmentations and boost a model’s performance on the validation set.

                                    ML GDE Victor Dibia (USA) wrote How to Build An Android App and Integrate Tensorflow ML Models sharing how to run machine learning models locally on Android mobile devices, How to Implement Gradient Explanations for a HuggingFace Text Classification Model (Tensorflow 2.0) explaining in 5 steps about how to verify the model is focusing on the right tokens to classify text. He also wrote how to finetune a HuggingFace model for text classification, using Tensorflow 2.0.

                                    ML GDE Karthic Rao (India) released a new series ML for JS developers with TFJS. This series is a combination of short portrait and long landscape videos. You can learn how to build a toxic word detector using TensorFlow.js.

                                    ML GDE Sayak Paul (India) implemented the DeiT family of ViT models, ported the pre-trained params into the implementation, and provided code for off-the-shelf inference, fine-tuning, visualizing attention rollout plots, distilling ViT models through attention. (code | pretrained model | tutorial)

                                    ML GDE Sayak Paul (India) and ML GDE Aritra Roy Gosthipaty (India) inspected various phenomena of a Vision Transformer, shared insights from various relevant works done in the area, and provided concise implementations that are compatible with Keras models. They provide tools to probe into the representations learned by different families of Vision Transformers. (tutorial | code)

                                    JAX/Flax

                                    ML GDE Aakash Nain (India) had a special talk, Introduction to JAX for ML GDEs, TFUG organizers and ML community network organizers. He covered the fundamentals of JAX/Flax so that more and more people try out JAX in the near future.

                                    ML GDE Seunghyun Lee (Korea) started a project, Training and Lightweighting Cookbook in JAX/FLAX. This project attempts to build a neural network training and lightweighting cookbook including three kinds of lightweighting solutions, i.e., knowledge distillation, filter pruning, and quantization.

                                    ML GDE Yucheng Wang (China) wrote History and features of JAX and explained the difference between JAX and Tensorflow.

                                    ML GDE Martin Andrews (Singapore) shared a video, Practical JAX : Using Hugging Face BERT on TPUs. He reviewed the Hugging Face BERT code, written in JAX/Flax, being fine-tuned on Google’s Colab using Google TPUs. (Notebook for the video)

                                    ML GDE Soumik Rakshit (India) wrote Implementing NeRF in JAX. He attempts to create a minimal implementation of 3D volumetric rendering of scenes represented by Neural Radiance Fields.

                                    Kaggle

                                    ML GDEs’ Kaggle notebooks were announced as the winner of Google OSS Expert Prize on Kaggle: Sayak Paul and Aritra Roy Gosthipaty’s Masked Image Modeling with Autoencoders in March; Sayak Paul’s Distilling Vision Transformers in April; Sayak Paul & Aritra Roy Gosthipaty’s Investigating Vision Transformer Representations; Soumik Rakshit’s Tensorflow Implementation of Zero-Reference Deep Curve Estimation in May and Aakash Nain’s The Definitive Guide to Augmentation in TensorFlow and JAX in June.

                                    ML GDE Luca Massaron (Italy) published The Kaggle Book with Konrad Banachewicz. This book details competition analysis, sample code, end-to-end pipelines, best practices, and tips & tricks. And in the online event, Luca and the co-author talked about how to compete on Kaggle.

                                    ML GDE Ertuğrul Demir (Turkey) wrote Kaggle Handbook: Fundamentals to Survive a Kaggle Shake-up covering bias-variance tradeoff, validation set, and cross validation approach. In the second post of the series, he showed more techniques using analogies and case studies.

                                    TFUG Chennai hosted ML Study Jam with Kaggle and created study groups for the interested participants. More than 60% of members were active during the whole program and many of them shared their completion certificates.

                                    TFUG Mysuru organizer Usha Rengaraju shared a Kaggle notebook which contains the implementation of the research paper: UNETR – Transformers for 3D Biomedical Image Segmentation. The model automatically segments the stomach and intestines on MRI scans.

                                    TFX

                                    ML GDE Sayak Paul (India) and ML GDE Chansung Park (Korea) shared how to deploy a deep learning model with Docker, Kubernetes, and Github actions, with two promising ways – FastAPI (for REST) and TF Serving (for gRPC).

                                    ML GDE Ukjae Jeong (Korea) and ML Engineers at Karrot Market, a mobile commerce unicorn with 23M users, wrote Why Karrot Uses TFX, and How to Improve Productivity on ML Pipeline Development.

                                    ML GDE Jun Jiang (China) had a talk introducing the concept of MLOps, the production-level end-to-end solutions of Google & TensorFlow, and how to use TFX to build the search and recommendation system & scientific research platform for large-scale machine learning training.

                                    ML GDE Piero Esposito (Brazil) wrote Building Deep Learning Pipelines with Tensorflow Extended. He showed how to get started with TFX locally and how to move a TFX pipeline from local environment to Vertex AI; and provided code samples to adapt and get started with TFX.

                                    TFUG São Paulo (Brazil) had a series of online webinars on TensorFlow and TFX. In the TFX session, they focused on how to put the models into production. They talked about the data structures in TFX and implementation of the first pipeline in TFX: ingesting and validating data.

                                    TFUG Stockholm hosted MLOps, TensorFlow in Production, and TFX covering why, what and how you can effectively leverage MLOps best practices to scale ML efforts and had a look at how TFX can be used for designing and deploying ML pipelines.

                                    Cloud AI

                                    ML GDE Chansung Park (Korea) wrote MLOps System with AutoML and Pipeline in Vertex AI on GCP official blog. He showed how Google Cloud Storage and Google Cloud Functions can help manage data and handle events in the MLOps system.

                                    He also shared the Github repository, Continuous Adaptation with VertexAI’s AutoML and Pipeline. This contains two notebooks to demonstrate how to automate to produce a new AutoML model when the new dataset comes in.

                                    TFUG Northwest (Portland) hosted The State and Future of AI + ML/MLOps/VertexAI lab walkthrough. In this event, ML GDE Al Kari (USA) outlined the technology landscape of AI, ML, MLOps and frameworks. Googler Andrew Ferlitsch had a talk about Google Cloud AI’s definition of the 8 stages of MLOps for enterprise scale production and how Vertex AI fits into each stage. And MLOps engineer Chris Thompson covered how easy it is to deploy a model using the Vertex AI tools.

                                    Research

                                    ML GDE Qinghua Duan (China) released a video which introduces Google’s latest 540 billion parameter model. He introduced the paper PaLM, and described the basic training process and innovations.

                                    ML GDE Rumei LI (China) wrote blog postings reviewing papers, DeepMind’s Flamingo and Google’s PaLM.

                                    Using research to make code review more equitable

                                    Posted by Emerson Murphy-Hill, Research Scientist, Central Product Inclusion, Equity, and Accessibility

                                    At Google, we often study our own software development work as a means to better understand and make improvements to our engineering practices. In a study that we recently published in Communications of the ACM, we describe how code review pushback varies depending on an author’s demographics. Such pushback, defined as “the perception of unnecessary interpersonal conflict in code review while a reviewer is blocking a change request”, turns out to affect some developers more than others.

                                    The study looked at pushback during the code review process and, in short, we found that:

                                    • Women faced 21% higher odds of pushback than men
                                    • Black+ developers faced 54% higher odds than White+ developers
                                    • Latinx+ developers faced 15% higher odds than White+ developers
                                    • Asian+ developers faced 42% higher odds than White+ developers
                                    • Older developers faced higher odds of pushback than younger developers

                                    We estimate that this excess pushback roughly costs Google more than 1,000 engineer hours per day – something we’re working to significantly reduce, along with unconscious bias during the review process, through solutions like anonymous code review.

                                    Last year, we explored the effectiveness of anonymous code review by asking 300 developers to do their code reviews without the author’s name at the top. Through this research, we found that code review times and review quality appeared consistent with and without anonymous review. We also found that, for certain types of review, it was more difficult for reviewers to guess the code’s author. To give you an idea, here’s what anonymous code review looks like today at Google in the Critique code review tool:

                                    In the screenshot above, changelist author names are replaced by anonymous animals, like in Google Docs, helping reviewers focus more on the code changes and less on the people making those changes.

                                    At Google, we strive to ensure there is equity in all that we do, including in our engineering processes and tools. Through continued experimentation with anonymous code review, we’re hoping to reduce gaps in pushback faced by developers from different demographic groups. And through this work, we want to inspire other companies to take a hard look at their own code reviews and to consider adopting anonymous author code review as part of their process as well.

                                    In the long run, we expect that increasing equity in developers’ experience will help Google – and our industry – make meaningful progress towards an inclusive development experience for all.

                                    Migrating from App Engine Memcache to Cloud Memorystore (Module 13)

                                    Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

                                    Introduction and background

                                    The previous Module 12 episode of the Serverless Migration Station video series demonstrated how to add App Engine Memcache usage to an existing app that has transitioned from the webapp2 framework to Flask. Today’s Module 13 episode continues its modernization by demonstrating how to migrate that app from Memcache to Cloud Memorystore. Moving from legacy APIs to standalone Cloud services makes apps more portable and provides an easier transition from Python 2 to 3. It also makes it possible to shift to other Cloud compute platforms should that be desired or advantageous. Developers benefit from upgrading to modern language releases and gain added flexibility in application-hosting options.

                                    While App Engine Memcache provides a basic, low-overhead, serverless caching service, Cloud Memorystore “takes it to the next level” as a standalone product. Rather than a proprietary caching engine, Cloud Memorystore gives users the option to select from a pair of open source engines, Memcached or Redis, each of which provides additional features unavailable from App Engine Memcache. Cloud Memorystore is typically more cost efficient at-scale, offers high availability, provides automatic backups, etc. On top of this, one Memorystore instance can be used across many applications as well as incorporates improvements to memory handling, configuration tuning, etc., gained from experience managing a huge fleet of Redis and Memcached instances.

                                    While Memcached is more similar to Memcache in usage/features, Redis has a much richer set of data structures that enable powerful application functionality if utilized. Redis has also been recognized as the most loved database by developers in StackOverflow’s annual developers survey, and it’s a great skill to pick up. For these reasons, we chose Redis as the caching engine for our sample app. However, if your apps’ usage of App Engine Memcache is deeper or more complex, a migration to Cloud Memorystore for Memcached may be a better option as a closer analog to Memcache.

                                    Migrating to Cloud Memorystore for Redis featured video


                                    Performing the migration

                                    The sample application registers individual web page “visits,” storing visitor information such as IP address and user agent. In the original app, the most recent visits are cached into Memcache for an hour and used for display if the same user continuously refreshes their browser during this period; caching is a one way to counter this abuse. New visitors or cache expiration results new visits as well as updating the cache with the most recent visits. Such functionality must be preserved when migrating to Cloud Memorystore for Redis.

                                    Below is pseudocode representing the core part of the app that saves new visits and queries for the most recent visits. Before, you can see how the most recent visits are cached into Memcache. After completing the migration, the underlying caching infrastructure has been swapped out in favor of Memorystore (via language-specific Redis client libraries). In this migration, we chose Redis version 5.0, and we recommend the latest versions, 5.0 and 6.x at the time of this writing, as the newest releases feature additional performance benefits, fixes to improve availability, and so on. In the code snippets below, notice how the calls between both caching systems are nearly identical. The bolded lines represent the migration-affected code managing the cached data.

                                    Switching from App Engine Memcache to Cloud Memorystore for Redis

                                    Wrap-up

                                    The migration covered begins with the Module 12 sample app (“START”). Migrating the caching system to Cloud Memorystore and other requisite updates results in the Module 13 sample app (“FINISH”) along with an optional port to Python 3. To practice this migration on your own to help prepare for your own migrations, follow the codelab to do it by-hand while following along in the video.

                                    While the code migration demonstrated seems straightforward, the most critical change is that Cloud Memorystore requires dedicated server instances. For this reason, a Serverless VPC connector is also needed to connect your App Engine app to those Memorystore instances, requiring more dedicated servers. Furthermore, neither Cloud Memorystore nor Cloud VPC are free services, and neither has an “Always free” tier quota. Before moving forward this migration, check the pricing documentation for Cloud Memorystore for Redis and Serverless VPC access to determine cost considerations before making a commitment.

                                    One key development that may affect your decision: In Fall 2021, the App Engine team extended support of many of the legacy bundled services like Memcache to next-generation runtimes, meaning you are no longer required to migrate to Cloud Memorystore when porting your app to Python 3. You can continue using Memcache even when upgrading to 3.x so long as you retrofit your code to access bundled services from next-generation runtimes.

                                    A move to Cloud Memorystore and today’s migration techniques will be here if and when you decide this is the direction you want to take for your App Engine apps. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, we plan to cover other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.

                                    Reach global markets as a Recommended for Google Workspace app

                                    Posted by Elena Kingbo, Program Manager, Google Workspace

                                    Today we announced our 2022 Recommended for Google Workspace apps. This program offers a distinct way for third-party developers to better reach Google Workspace users and attract new customers to their apps. So, for those developers who may be interested in it in the future, we wanted to walk through the basics of what the program is and how to apply for it.

                                    What is the Google Workspace Marketplace?

                                    The Google Workspace Marketplace is the first place Google Workspace administrators and users look when they want to extend or enhance their Google Workspace experience. The Marketplace can be accessed within most first-party Google Workspace apps, including Gmail, Drive, Docs, Sheets, Slides, Forms, Calendar, and Classroom, as well as at workspace.google.com/marketplace.

                                    Launch Marketplace from your favorite Google Workspace app by clicking the “+”.

                                    The Google Workspace Marketplace is the first place Google Workspace administrators and users look when they want to extend or enhance their Google Workspace experience. The Marketplace can be accessed within most first-party Google Workspace apps, including Gmail, Drive, Docs, Sheets, Slides, Forms, Calendar, and Classroom, as well as at workspace.google.com/marketplace.

                                    Developers who want to build and deploy apps to the Marketplace can either use their own preferred coding language or leverage Google Apps Script, our serverless platform. You can learn more about building apps and publishing them to the Marketplace in our developer documentation.

                                    What is the Recommended for Google Workspace program?

                                    The Recommended for Google Workspace program identifies and promotes a select number of Google Workspace applications that are secure, reliable, well-integrated with Google Workspace, and loved by users.

                                    Partners who submit their apps will be evaluated based on the quality of their solution, their strategic investment in Google Workspace integrations, and security and privacy posture. In addition, all partners will be required to complete a third-party security assessment in the final stage of the assessment. You can sign up for our Google Workspace developers newsletter to be notified when the next application window opens up.

                                    What it means to be a Recommended app

                                    Google Workspace customers are often looking for high-quality, secure apps they can install to enhance their Workspace experience. Since recommended apps have exceeded our highest security and reliability standards, they are the first apps we recommend to customers and among the first apps users see when they visit the Marketplace. Recommended partners will also receive new and enhanced benefits, including technical advisory services and early access to APIs.

                                    There have been more than 4.8 billion app installs on the Marketplace. These apps are an integral part of the Google Workspace experience and users are continually looking for new ways to extend the value of Google Workspace. Creating a Google Workspace app is a fantastic opportunity for innovative developers interested in enhancing the Google Workspace experience. And, for those developers who truly want to be set apart as a trusted app on the Marketplace, the Recommended for Google Workspace program offers an unique way to reach new customers.

                                    Explore our Recommended for Google Workspace apps on the Google Workspace Marketplace.

                                    South African developers build web application to help local athletes

                                    Posted by Aniedi Udo-Obong, Sub-Saharan Africa Regional Lead, Google Developer Groups

                                    Lesego Ndlovu and Simon Mokgotlhoa have stayed friends since they were eight years old, trading GameBoy cartridges and playing soccer. They live three houses away from each other in Soweto, the biggest township in South Africa, with over one million residents. The two friends have always been fascinated by technology, and by the time the duo attended university, they wanted to start a business together that would also help their community.

                                    Lesego Ndlovu and Simon Mokgotlhoa sitting at a desk on their computers

                                    After teaching themselves to code and attending Google Developer Groups (GDG) events in Johannesburg, they built a prototype and launched a chapter of their own (GDG Soweto) to teach other new developers how to code and build technology careers.

                                    Building an app to help their community

                                    Lesego and Simon wanted to build an application that would help the talented soccer players in their community get discovered and recruited by professional soccer teams. To do that, they had to learn to code.

                                    Lesego Ndlovu and Simon Mokgotlhoa holding their phones towards the screen showcasing the Ball Talent app

                                    “We always played soccer, and we saw talented players not get discovered, so, given our interest in sports and passion for technology, we wanted to make something that could change that narrative,” Lesego says. “We watched videos on the Chrome Developers YouTube channel and learned HTML, CSS, and JavaScript, but we didn’t know how to make an app, deliver a product, or start a business. Our tech journey became a business journey. We learned about the code as the business grew. It’s been a great journey.”

                                    After many all-nighters learning frontend development using HTML, CSS, and JavaScript, and working on their project, they built BallTalent, a Progressive Web App (PWA), that helps local soccer players in their neighborhood get discovered by professional soccer clubs. They record games in their neighborhood and upload them to the app, so clubs can identify new talent.

                                    “We tested our prototype with people, and it seemed like they really loved it, which pushed us to keep coding and improving on the project,” says Simon. “The application is currently focused on soccer, but it’s built it in a way that it can focus on other sports.”

                                    In 2019, when BallTalent launched, the project placed in the top 5 of one of South Africa’s most prestigious competitions, Diageo Social Tech Startup Challenge. BallTalent has helped local soccer players match with professional teams, benefiting the community. Simon and Lesego plan to release version two soon, with a goal of expanding to other sports.

                                    Learning to code with web technologies and resources

                                    Lesego and Simon chose to watch the Chrome Developers YouTube channel to learn to code, because it was free, accessible, and taught programming in ways that were easy to understand. Preferring to continue to use free Google tools because of their availability and ease of use, Lesego and Simon used Google developer tools on Chrome to build and test the BallTalent app, which is hosted on Google Cloud Platform.

                                    BallTalent Shows Youth Talent to the Worlds Best Scouts and Clubs

                                    They used NodeJS as their backend runtime environment to stay within the Google ecosystem–NodeJS is powered by the V8 JavaScript engine, which is developed by the Chromium Project. They used a service worker codelab from Google to allow users to install the BallTalent PWA and see partial content, even without an internet connection.

                                    We are focused on HTML, CSS, JavaScript, frontend frameworks like Angular, and Cloud tools like Firebase, to be able to equip people with the knowledge of how to set up an application,” says Simon.

                                    Moving gif of soccer players playing on a soccer field

                                    BallTalent shares sample footage of a previous match: Mangaung United Vs Bizana Pondo Chiefs, during the ABC Motsepe Play Offs

                                    “Google has been with us the whole way,” says Simon.

                                    Contributing to the Google Developer community

                                    Because of their enthusiasm for web technologies and positive experience learning to code using Google tools, Lesego and Simon were enthusiastic about joining a Google Developer Community. They became regular members at GDG Johannesburg and went to DevFest South Africa in 2018, where they got inspired to start their own GDG chapter in Soweto. The chapter focuses on frontend development to meet the needs of a largely beginner developer membership and has grown to 500+ members.

                                    Looking forward to continued growth

                                    The duo is now preparing to launch the second version of their BallTalent app, which gives back to their community by pairing local soccer talent with professional teams seeking players. In addition, they’re teaching new developers in their township how to build their own apps, building community and creating opportunities for new developers. Google Developer Groups are local community groups for developers interested in learning new skills, teaching others, and connecting with other developers. We encourage you to join us, and if you’re interested in becoming a GDG organizer like Simon and Lesego, we encourage you to apply.

                                    Finding courage and inspiration in the developer community

                                    Posted by Monika Janota

                                    How do we empower women in tech and equip them with the skills to help them become true leaders? One way is learning from others’ successes and failures. Web GDEs—Debbie O’Brien, Julia Miocene, and Glafira Zhur—discuss the value of one to one mentoring and the impact it has made on their own professional and personal development.

                                    A 2019 study showed that only 25% of keynote speakers at tech events are women, meanwhile 70% of female speakers mentioned being the only woman on a conference panel. One way of changing that is by running programs and workshops with the aim of empowering women and providing them with the relevant soft skills training, including public speaking, content creation, and leadership. Among such programs are the Women Developer Academy (WDA) and the Road to GDE, both run by Google’s developer communities.

                                    With more than 1000 graduates around the world, WDA is a program run by Women Techmakers for professional IT practitioners. To equip women in tech with speaking and presentation skills, along with confidence and courage, training sessions, workshops, and mentoring meetings are organized. Road to GDE, on the other hand, is a three-month mentoring program created to support people from historically underrepresented groups in tech on their path to becoming experts. What makes both programs special is the fact that they’re based on a unique connection between mentor and mentee, direct knowledge sharing, and an individualized approach.

                                    Photo of Julia Miocene speaking at a conference Julia Miocene

                                    Some Web GDE community members have had a chance to be part of the mentoring programs for women as both mentors and mentees. Frontend developers Julia Miocene and Glafira Zhur are relatively new to the GDE program. They became Google Developers Experts in October 2021 and January 2022 respectively, after graduating from the first edition of both the Women Developer Academy and the Road to GDE; whilst Debbie O’Brien has been a member of the community and an active mentor for both programs for several years. They have all shared their experiences with the programs in order to encourage other women in tech to believe in themselves, take a chance, and to become true leaders.

                                    Different paths, one goal

                                    Although all three share an interest in frontend development, each has followed a very different path. Glafira Zhur, now a team leader with 12 years of professional experience, originally planned to become a musician, but decided to follow her other passion instead. A technology fan thanks to her father, she was able to reinstall Windows at the age of 11. Julia Miocene, after more than ten years in product design, was really passionate about CSS. She became a GDE because she wanted to work with Chrome and DevTools. Debbie is a Developer Advocate working in the frontend area, with a strong passion for user experience and performance. For her, mentoring is a way of giving back to the community, helping other people achieve their dreams, and become the programmers they want to be. At one point while learning JavaScript, she was so discouraged she wanted to give it up, but her mentor convinced her she could be successful. Now she’s returning the favor.

                                    Photo of Debbie O'Brien and another woman in a room smiling at the camera

                                    Debbie O’Brien

                                    As GDEs, Debbie, Glafira, and Julia all mention that the most valuable part of becoming experts is the chance to meet people with similar interests in technology, to network, and to provide early feedback for the web team. Mentoring, on the other hand, enables them to create, it boosts their confidence and empowers them to share their skills and knowledge—regardless of whether they’re a mentor or a mentee.

                                    Sharing knowledge

                                    A huge part of being a mentee in Google’s programs is learning how to share knowledge with other developers and help them in the most effective way. Many WDA and Road to GDE participants become mentors themselves. According to Julia, it’s important to remember that a mentor is not a teacher—they are much more. The aim of mentoring, she says, is to create something together, whether it’s an idea, a lasting connection, a piece of knowledge, or a plan for the future.

                                    Glafira mentioned that she learned to perceive social media in a new way—as a hub for sharing knowledge, no matter how small the piece of advice might seem. It’s because, she says, even the shortest Tweet may help someone who’s stuck on a technical issue that they might not be able to resolve without such content being available online. Every piece of knowledge is valuable. Glafira adds that, “Social media is now my tool, I can use it to inspire people, invite them to join the activities I organize. It’s not only about sharing rough knowledge, but also my energy.”

                                    Working with mentors who have successfully built an audience for their own channels allows the participants to learn more about the technical aspects of content creation—how to choose topics that might be interesting for readers, set up the lighting in the studio, or prepare an engaging conference speech.

                                    Learning while teaching

                                    From the other side of the mentor—mentee relationship, Debbie O’Brien says the best thing about mentoring is seeing the mentees grow and succeed: “We see in them something they can’t see in themselves, we believe in them, and help guide them to achieve their goals. The funny thing is that sometimes the advice we give them is also useful for ourselves, so as mentors we end up learning a lot from the experience too.”

                                    TV screenin a room showing and image od Glafira Zhur

                                    Glafira Zhur

                                    Both Glafira and Julia state that they’re willing to mentor other women on their way to success. Asked what is the most important learning from a mentorship program, they mention confidence—believing in yourself is something they want for every female developer out there.

                                    Growing as a part of the community

                                    Both Glafira and Julia mentioned that during the programs they met many inspiring people from their local developer communities. Being able to ask others for help, share insights and doubts, and get feedback was a valuable lesson for both women.

                                    Mentors may become role models for the programs’ participants. Julia mentioned how important it was for her to see someone else succeed and follow in their footsteps, to map out exactly where you want to be professionally, and how you can get there. This means learning not just from someone else’s failures, but also from their victories and achievements.

                                    Networking within the developer community is also a great opportunity to grow your audience by visiting other contributors’ podcasts and YouTube channels. Glafira recalls that during the Academy, she received multiple invites and had an opportunity to share her knowledge on different channels.

                                    Overall, what’s even more important than growing your audience is finding your own voice. As Debbie states: “We need more women speaking at conferences, sharing knowledge online, and being part of the community. So I encourage you all to be brave and follow your dreams. I believe in you, so now it’s time to start believing in yourself.”

                                    How to use App Engine Memcache in Flask apps (Module 12)

                                    Posted by Wesley Chun

                                    Background

                                    In our ongoing Serverless Migration Station series aimed at helping developers modernize their serverless applications, one of the key objectives for Google App Engine developers is to upgrade to the latest language runtimes, such as from Python 2 to 3 or Java 8 to 17. Another objective is to help developers learn how to move away from App Engine legacy APIs (now called “bundled services”) to Cloud standalone equivalent services. Once this has been accomplished, apps are much more portable, making them flexible enough to:

                                    In today’s Module 12 video, we’re going to start our journey by implementing App Engine’s Memcache bundled service, setting us up for our next move to a more complete in-cloud caching service, Cloud Memorystore. Most apps typically rely on some database, and in many situations, they can benefit from a caching layer to reduce the number of queries and improve response latency. In the video, we add use of Memcache to a Python 2 app that has already migrated web frameworks from webapp2 to Flask, providing greater portability and execution options. More importantly, it paves the way for an eventual 3.x upgrade because the Python 3 App Engine runtime does not support webapp2. We’ll cover both the 3.x and Cloud Memorystore ports next in Module 13.

                                    Got an older app needing an update? We can help with that.

                                    Adding use of Memcache

                                    The sample application registers individual web page “visits,” storing visitor information such as the IP address and user agent. In the original app, these values are stored immediately, and then the most recent visits are queried to display in the browser. If the same user continuously refreshes their browser, each refresh constitutes a new visit. To discourage this type of abuse, we cache the same user’s visit for an hour, returning the same cached list of most recent visits unless a new visitor arrives or an hour has elapsed since their initial visit.

                                    Below is pseudocode representing the core part of the app that saves new visits and queries for the most recent visits. Before, you can see how each visit is registered. After the update, the app attempts to fetch these visits from the cache. If cached results are available and “fresh” (within the hour), they’re used immediately, but if cache is empty, or a new visitor arrives, the current visit is stored as before, and this latest collection of visits is cached for an hour. The bolded lines represent the new code that manages the cached data.

                                    Adding App Engine Memcache usage to sample app

                                    Wrap-up

                                    Today’s “migration” began with the Module 1 sample app. We added a Memcache-based caching layer and arrived at the finish line with the Module 12 sample app. To practice this on your own, follow the codelab doing it by-hand while following the video. The Module 12 app will then be ready to upgrade to Cloud Memorystore should you choose to do so.

                                    In Fall 2021, the App Engine team extended support of many of the bundled services to next-generation runtimes, meaning you are no longer required to migrate to Cloud Memorystore when porting your app to Python 3. You can continue using Memcache in your Python 3 app so long as you retrofit the code to access bundled services from next-generation runtimes.

                                    If you do want to move to Cloud Memorystore, stay tuned for the Module 13 video or try its codelab to get a sneak peek. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, we hope to one day cover other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.

                                    Celebrating leaders in AAPI communities

                                    Posted by Google Developer Studio

                                    In recognition of Asian Americans and Pacific Islanders Heritage Month, we are speaking with mentors and leaders in tech and who identify as part of the AAPI community. Many of the influential figures we feature are involved with and help champion inclusivity programs like Google Developer Experts and Google Developer Student Clubs, while others work on leading in product areas like TensorFlow and drive impact through their line of work and communities.

                                    On that note, we are honoring this year’s theme of “Advancing Leaders Through Collaboration” by learning more about the power of mentorship, advice they’ve received from other leaders, and their biggest accomplishments.

                                    Read more about leads in the AAPI community below.

                                    Ben Hong

                                    Senior Staff Developer Experience Engineer at Netlify

                                    What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

                                    There is a lot of advice out there on how to get the most out of your career by climbing the ladder and getting leadership roles. Before you embark on that journey, first ask yourself the question “Why do I want this?”

                                    Becoming a leader comes with a lot of glitz and glamor, but the reality is that it carries a huge weight of responsibility because the decisions and actions you take as a leader will impact the lives of those around you in significant ways you can’t foresee.

                                    As a result, the key to becoming the best leader you can be is to:

                                    1. Establish what your values and principles are
                                    2. Align them to the actions you take each and every day

                                    Because at the end of the day, leaders are often faced with difficult decisions that lead to an uncertain future. And without core values and principles to guide you as an individual, you run the risk of being easily swayed by short term trade offs that could result in a long term loss.

                                    This world needs leaders who can stand their ground against the temptations of short-term wins and make the best decisions they can while fighting for those that follow them. If you stand firm in your values and listen to those around you, you’ll be able to create profound impact in your community.

                                    Taha Bouhsine

                                    Data Scientist and GDSCUIZ Lead

                                    What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

                                    Create a journey worth taking. You will face many challenges and a new set of problems. You will start asking a lot of questions as everything seems to be unfamiliar.

                                    Things get much lighter if you are guided by a mentor, as you will get guidance on how to act in this new chapter of life. In your early days, invest as much as you can in building and nurturing a team, as it will save you a lot of time along the road. Surround yourself with real people who take the initiative, get to the action, and are willing to grow and learn, nurture their skills and guide them towards your common goal. Don’t try to be a people pleaser as it’s an impossible mission.

                                    Your actions will offend some people one way or the other. That’s ok as you should believe in your mission, create a clear plan with well-defined tasks and milestones, and be firm with your decision. In the end, responsibility is yours to bear, so at least take it on something you decided, not something that was forced upon you by others.

                                    Finally, when there is fire, look for ways to put it out. Take care of your soul, and enjoy the journey!

                                    Huyen Tue Dao

                                    Android Developer, Trello

                                    What do you love most about being a part of the developer community?

                                    It has been the most rewarding and critical part of my career to meet other developers, learning and sharing knowledge and getting to know them as human beings.

                                    Development is a job of constant learning, whether it is the latest technology, trends, issues, and challenges or the day-to-day intricacies and nuances of writing specialized code and solving problems in efficient and elegant ways. I don’t think I’d have the tools to solve issues large and small without the sharing of knowledge and experience of the developer community. If you’re having a problem of any kind, chances are that someone has had the same challenges. You can take comfort that you can probably find the answer or at least find people that can help you. You can also feel confident that if you discovered something new or learned important lessons, someone will want to hear what you have to say.

                                    I love seeing and being part of this cycle and interchange; as we pool our experience, our knowledge, and insights, we become stronger and more skilled as a community. I would not be the engineer or person that I am without the opportunities of this exchange.

                                    Just as important, though, is the camaraderie and support of those who do what I do and love it. I have been so fortunate to have been in communities that have been open and welcoming, ready to make connections and form networks, eager to celebrate victories and commiserate with challenges. Regardless of the technical and personal challenges of the everyday that may get to me, there are people that understand and can support me and provide brilliantly diverse perspectives of different industries, countries, cultures, and ages.

                                    Malak Magdy Ali

                                    Google Developer Student Club Lead at Canadian International College, Egypt

                                    What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

                                    The best piece of advice I can give to new leaders is to have empathy. Having empathy will make you understand people’s actions and respect their feelings. This will make for stronger teams.

                                    Also, give others a space to lead. Involve your team in making decisions; they come up with great ideas that can help you and teammates learn from each other. In this process, trust is also built, resulting in a better quality product.

                                    Finally, don’t underestimate yourself. Do your best and involve your team to discuss the overall quality of your work and let them make recommendations.