Amazon Lex is now available in the Asia Pacific (Seoul) and Africa (Cape Town) regions

Starting today, Amazon Lex  is available in the Asia Pacific (Seoul) and Africa (Cape Town) regions. Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex combines advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text. This enables you to build applications with engaging users experiences and lifelike interactions. With Amazon Lex, you can easily create sophisticated, natural language, conversational bots (“chatbots”), virtual agents and IVR systems.

AWS Ground Station announces Licensing Accelerator

AWS is announcing Licensing Accelerator, a new AWS Ground Station feature which provides commercial businesses, space start-ups, and universities access to resources to help them more efficiently secure spectrum licenses required for their operations and missions. Licensing accelerator is free-of-charge to AWS Ground Station customers. AWS Ground Station is a fully managed service that lets customers control satellite communications, process satellite data, and scale their satellite operations. With Licensing Accelerator, AWS Ground Station customers can launch and scale their spacecraft operations faster by leveraging the latest, centrally located information about satellite licensing regulations such as space station licensing, remote sensing licenses, and International Telecommunications Union (ITU) coordination.

From Beginner to Machine Learning Instructor In A Year

Posted by Salim Abid, MENA Regional Lead, Developer Relations

Banner that reads Google Developer Student Clubs, Misr University for Science and Technology (MUST). Includes overhead image of person coding on a laptop

Yara Elkady, Google Developer Student Club (GDSC) Lead, can trace her passion for tech all the way back to a single moment. She was sitting in computer class when her middle school teacher posed a question to the class:

“Did you know that you can create apps and games like the ones that you spend so much time on?”

It was a simple question, but it was enough to plant the seed that would define the trajectory of Yara’s career. Following in the footsteps of so many beginners before her, Yara did a Google search to find out more about creating apps. She didn’t realize it at the time, but Yara had just taken her first steps down the path to becoming a developer.

Knowing that she wanted to pursue tech further, Yara went to college at Misr University for Science and Technology (MUST) in Giza, Egypt to study computer science. In her second year, she had begun reading more about artificial intelligence. Yara was blown away by the potential of training a machine to make decisions on its own. With machine learning, she could pursue more creative ideas that went beyond what was possible with traditional programming. As Yara explains, “It felt like magic”. Still, she felt lost like any beginner interested in AI.

Enter Google Developer Student Clubs

Yara first discovered the GDSC chapter at MUST through her school’s social media page. For the entirety of her second year, Yara attended workshops and saw firsthand how GDSC events could leave an impact on students aspiring to become developers. With help from Google Developer Student Clubs, Yara was able to grow her skills as a developer and connect with peers who shared her interests. At the end of the year, Yara applied to be a Lead so that she could help more students engage with the community. Not too long after, Yara was accepted as a GDSC Lead for the 2020-2021 season!

A classroom of people attend a GDSC MUST speaker session

A GDSC MUST speaker session

As part of becoming a GDSC Lead, Yara enrolled in the MENA DSC Leads Academy to receive hands-on training in various Google technologies. Despite being only the first time the Academy had ever been hosted (both in person and virtually), 100+ Leads from 150 GDSC chapters attended over the course of six weeks. Yara applied to the Machine Learning track and was chosen for the program. During the course, Yara mastered advanced machine learning concepts, including classical ML models, deep learning, data manipulation, and TensorFlow training. She also got to work with other Leads on advanced machine learning projects, helping her gain even more confidence in her ML knowledge.

Soon after passing the program, Yara collaborated with the GDSC Leads she met during the course to host a one-month ML track to pass on the knowledge they had learned to the GDSC community. Through the sessions she hosted, Yara was contacted by BambooGeeks, a startup that creates training opportunities for local tech aspirants to help them become industry-ready. Yara was offered a job as a machine learning instructor, and could now create sessions for the largest audience of trainees she’d ever worked with.

The road to certification

Yara didn’t realize it yet, but even more opportunities were headed her way. She learned from the GDSC MENA program manager that GDSC Leads would have the opportunity to take the TensorFlow Certification exam, if they wished to take it. It wouldn’t be easy, but Yara knew she had all the resources she needed to succeed. She wasted no time and created a study group with other GDSC Leads working to get certified. Together, Yara and her fellow Leads pulled endless all-nighters over the next few months so that they could skill up for the exam and support each other through the arduous study process. They also worked with Elyes Manai, a ML Google Developer Expert, who gave them an overview of the exam and recommended resources that would help them pass.

Thanks to those resources, support from her peers, and tons of hard work, Yara passed the exam and received her TensorFlow certification! And she wasn’t the only one. 11 other MENA GDSC Leads also passed the exam to receive their certifications. Yara and her study partners were the first women in Egypt to be featured in the TensorFlow Certificate Network, and Yara became one of 27 people in Africa to receive the TensorFlow Developer Certificate!

Image of Yara Elkady's TensorFlow Developer Certificate

Yara’s TensorFlow Developer Certificate

When Yara looks back at how she was able to fast track from beginner to certified machine learning developer in just a year, she credits Google Developer Student Clubs with:

  • Offering advanced Machine Learning training
  • Fostering connections with other Leads to host study jams
  • Providing guidance from machine learning GDEs
  • TensorFlow certification exam prep
  • Exposure to opportunities that enabled her to inspire others
  • Endless community support

The truth is, students like Yara make Google Developer Student Clubs special by sharing their knowledge with the community and building a support system with their peers that extends far beyond the classroom.

On the importance of community, Yara says it best:

“Reaching your goals is a much more enjoyable process when you have someone with you on the same journey, to share your ups and downs, and push you to do more when you feel like quitting. Your success becomes their success and that gives more meaning to your accomplishments.”

If you’re a student who is ready to join your own Google Developer Student Club community, find one near you here.

Amazon ECR adds the ability to replicate individual repositories to other regions and accounts

Today, Amazon Elastic Container Registry (ECR) launched the ability to replicate specific repositories to accounts or regions, and see when images were replicated through the ECR API. This gives you granular control to replicate images within repositories you want, instead of replicating all images in a registry, and the ability to automate actions through the new DescribeImageReplicationStatus API whenever images are replicated.

A partir de ahora, los usuarios de Amazon EMR Studio podrán utilizar la autenticación basada en IAM o la federación de IAM, además de AWS Single Sign-On

Amazon EMR Studio es un entorno de desarrollo integrado (IDE) que facilita a los científicos e ingenieros de datos el desarrollo, la visualización y la corrección de aplicaciones de ingeniería y de ciencias de datos escritas en R, Python, Scala y PySpark. Hoy incorporamos opciones adicionales de autenticación con EMR Studio. Antes de esta novedad, para iniciar sesión en EMR Studio, era necesario integrar el proveedor de identidad (IdP) con AWS Single Sign-on (AWS SSO). Con esta versión, ahora puede elegir utilizar la autenticación de AWS Identity and Access Management (IAM) o utilizar la federación de IAM con las credenciales corporativas para iniciar sesión en EMR Studio, además de utilizar AWS SSO.

Ya están disponibles las métricas de solicitud de Amazon CloudWatch para los puntos de acceso de Amazon S3

Los clientes que utilicen puntos de acceso de Amazon S3 ahora pueden configurar métricas de solicitud de Amazon CloudWatch. Con este lanzamiento, ahora se pueden generar métricas de solicitud de S3 para todos los objetos de un bucket, o bien puede generar métricas para combinaciones específicas de prefijo, etiquetas de objeto o puntos de acceso. Con los puntos de acceso de S3, puede crear fácilmente los controles de acceso adecuados para el conjunto de datos compartido, y ahora, gracias a la compatibilidad con el filtrado por puntos de acceso, puede monitorear los patrones de solicitud por controles de acceso. Puede utilizar la consola de administración, el SDK o la API de S3, o bien AWS CloudFormation, para habilitar las métricas de solicitud de S3. Las métricas están disponibles en intervalos de 1 minuto y se pueden monitorear tanto en la consola de Amazon S3 como en la consola de Amazon CloudWatch.

Skip the setup— Run code directly from Google Cloud’s documentation

Posted by Abby Carey, Developer Advocate

Blog header

Long gone are the days of looking for documentation, finding a how-to guide, and questioning whether the commands and code samples actually work.

Google Cloud recently added a Cloud Shell integration within each and every documentation page.

This new functionality lets you test code in a preprovisioned virtual machine instance while learning about Google Cloud services. Running commands and code from the documentation cuts down on context switching between the documentation and a terminal window to run the commands in a tutorial.

This gif shows how Google Cloud’s documentation uses Cloud Shell, letting you run commands in a quickstart within your Cloud Shell environment.

gif showing how Google Cloud’s documentation uses Cloud Shell, letting you run commands in a quickstart within your Cloud Shell environment.

If you’re new to developing on Google Cloud, this creates a low barrier to entry for trying Google Cloud services and APIs. After activating billing verification with your Google Cloud account, you can test services that have a free tier at no charge, like Pub/Sub and Cloud Vision.

  1. Open a Google Cloud documentation page (like this Pub/Sub quickstart).
  2. Sign into your Google account.
  3. In the top navigation, click Activate Cloud Shell.
  4. Select your project or create one if you don’t already have one. You can select a project by running the gcloud config set project command or by using this drop-down menu:
    image showing how to select a project
  5. Copy, paste, and run your commands.

If you want to test something a bit more adventurous, try to deploy a containerized web application, or get started with BigQuery.

A bit about Cloud Shell

If you’ve been developing on Google Cloud, chances are you’ve already interacted with Cloud Shell in the Cloud Console. Cloud Shell is a ready-to-go, online development and operations environment. It comes preinstalled with common command-line tools, programming languages, and the Cloud SDK.

Just like in the Cloud Console, your Cloud Shell terminal stays open as you navigate the site. As you work through tutorials within Google Cloud’s documentation, the Cloud Shell terminal stays on your screen. This helps with progressing from two connected tutorials, like the Pub/Sub quickstart and setting up a Pub/Sub Proxy.

Having a preprovisioned environment setup by Google eliminates the age old question of “Is my machine the problem?” when you eventually try to run these commands locally.

What about code samples?

While Cloud Shell is useful for managing your Google Cloud resources, it also lets you test code samples. If you’re using Cloud Client Libraries, you can customize and run sample code in the Cloud Shell’s built in code editor: Cloud Shell Editor.

Cloud Shell Editor is Cloud Shell’s built-in, browser-based code editor, powered by the Eclipse Theia IDE platform. To open it, click the Open Editor button from your Cloud Shell terminal:

Image showing how to open Cloud Shell Editor

Cloud Shell Editor has rich language support and debuggers for Go, Java, .Net, Python, NodeJS and more languages, integrated source control, local emulators for Kubernetes, and more features. With the Cloud Shell Editor open, you can then walk through a client library tutorial like Cloud Vision’s Detect labels guide, running terminal commands and code from one browser tab.

Open up a Google Cloud quickstart and give it a try! This could be a game-changer for your learning experience.

Cloud Shell Editor has rich language support and debuggers for Go, Java, .Net, Python, NodeJS and more languages, integrated source control, local emulators for Kubernetes, and more features. With the Cloud Shell Editor open, you can then walk through a client library tutorial like Cloud Vision’s Detect labels guide, running terminal commands and code from one browser tab.

Open up a Google Cloud quickstart and give it a try! This could be a game-changer for your learning experience.

AWS Amplify CLI and Admin UI is now generally available in US West (N. California), Europe (Paris), Europe (Stockholm), South America (São Paulo), and Middle East (Bahrain)

AWS Amplify offers a fully managed static web hosting service that accelerates your application release cycle by providing a simple CI/CD workflow for building and deploying full-stack static web applications. Simply connect your application’s code repository in the console, and changes to your frontend and backend are deployed in a single workflow on every code commit.

SageMaker Studio enables interactive Spark based data processing from Studio Notebooks

Amazon SageMaker announces a new set of capabilities that will enable interactive Spark based data processing from SageMaker Studio Notebooks. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. With a single click, data scientists and developers can quickly spin up Studio Notebooks to interactively explore datasets and build ML models.

Optimize your Amazon Forecast model with the accuracy metric of your choice

We’re excited to announce that in Amazon Forecast, you can now select the accuracy metric of your choice to direct AutoML to optimize training a predictor for the selected accuracy metric. Additionally, we have added three more accuracy metrics to evaluate your predictor – average weighted quantile loss (Average wQL), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE).