Editor’s note:This is the second post in an ongoing tips series. If your company recently switched to Chrome OS, use these tips to get up to speed. If you’re an IT administrator, pass these tips along to your users to help them stay productive.
Last month, we shared tips on how to use a Chromebook if you switched from previously using a Mac. This month, we’ll explore similar topics for PC users, answering questions about keyboard shortcuts and finding files. If you’ve recently switched from using a PC with a Windows operating system to a Chromebook with Chrome OS, here are four must-read tips to help you get started.
1. How do I locate my apps and files? You’re probably used to accessing files and apps via the Start Menu on the bottom left-hand side of your PC. On a Chromebook, you access files and apps from the Launcher, which is the button that looks like a circle with a dot in the bottom of your screen.
Here’s how to use the Launcher: If you don’t have a touchscreen, click anywhere on the Launcher button. If you have a touchscreen, drag your finger up from the bottom part of your screen (we call this the “app shelf”). The Launcher will pop up—this is where you can access all of your apps, including Gmail, Google Drive, Docs, Sheets and Slides, as well as YouTube.
If you need to find a specific app, document or file, you can search in the search bar at the top of Launcher. Using predictive text based on machine learning, Google Search helps you find what you need quickly, whether it’s tracking down an app to use, searching the web, or finding a specific Doc you created.
2. How do I take a screenshot? If you want to take a screenshot of your entire screen on a Chromebook, press Ctrl + the Show windows button (Ctrl + F5). To capture a partial screenshot, press Shift + Ctrl + Show windows (Shift + Ctrl + F5), then click and drag your cursor over the exact area you want to capture. To take a screenshot on tablets, press the Power button + the Volume down button at the same time.
While we’re on the topic, there are loads of ways to work more efficiently with keyboard shortcuts. Check out our Help Center for other shortcut ideas.
3. How do I change my settings? For users familiar with a PC, the control panel is the place where you access settings like screen resolution, keyboard preferences, and privacy and security. On a Chromebook, you’ll find all of these options in Settings, which can be accessed in the bottom right of your screen.
Click the time in the bottom right corner of your screen. It will pull up different tools for you to use.
Click the gear/settings icon in the top right.
Scroll down to the setting options that interest you and make sure they match your needs.
Note: clicking “Advanced” at the bottom will get you even more options for settings. You can also enter keywords in the search bar to locate a specific setting.
4. How do I set up right-click? Right-click works a little differently on a Chromebook than on a PC (where you typically hit Shift + F10). You can either press the touchpad with two fingers to open the right-click menu, or you can click “Alt” and use just one finger on the touchpad. Once you do, you can scroll, moving left and right to move horizontally or up and down to move vertically. You can also switch your scroll direction. This article explains how, along with other helpful touchscreen tips.
Resources for IT admins to help users If you’re an IT admin, use these tips to help former PC users adjust to Chromebooks, or keep an eye out for more advice you can share—we’ll link new posts here as we publish them.
Last year our TensorFlow teamannounced theWhat-If Tool, an interactive visual interface designed to help you visualize your datasets and better understand the output of your TensorFlow models. Today, we’re announcing a new integration with the What-If Tool to analyze your models deployed onAI Platform. In addition to TensorFlow models, you can also use the What-If Tool for your XGBoost and Scikit Learn models deployed on AI Platform.
As AI models grow in complexity, understanding the inner workings of a model makes it possible to explain and interpret the outcomes driven by AI. As a result, AI explainability has become a critical requirement for most organizations in industries like financial services, healthcare, media and entertainment, and technology.
With this integration, AI Platform users can develop a deeper understanding of how their models work under different scenarios, and build rich visualizations to explain model performance to business users and other stakeholders of AI within an enterprise.
With just one method call, you can connect your AI Platform model to the What-If Tool:
You can use this new integration from AI Platform Notebooks, Colab notebooks, or locally via Jupyter notebooks. In this post, we’ll walk you through an example using an XGBoost model deployed on AI Platform.
Getting started: deploying a model to AI Platform In order to use this integration, you’ll need a model deployed on Cloud AI Platform. Once you’ve trained a model, you can deploy it to AI Platform using the gcloud CLI. If you don’t yet have a Cloud account, we’ve got one notebook that runs the What-if Tool on a public Cloud AI Platform model so you can easily try out the integration before you deploy your own.
The XGBoost example we’ll be showing here is a binary classification model for predicting whether or not a mortgage application will be approved, trained on this public dataset. In order to deploy this model, we’ve exported it to a .bst model file (the format XGBoost uses) and uploaded this to a Cloud Storage bucket in our project. We can deploy it with this command (make sure to define the environment variables when you run this):
Connecting your model to the What-If Tool Once your model has been deployed, you can view its performance on a dataset in the What-If Tool by setting up a WitConfigBuilder object as shown in the code snippet above. Provide your test examples in the format expected by the model, whether that be a list of JSON dictionaries, JSON lists, or tf.Example protos. Your test examples should include the ground truth labels so you can explore how different features impact your model’s predictions.
Point the tool at your model through your project name, model name, and model version, and optionally set the name of the feature in the dataset that the model is trying to predict.
Additionally, if you want to compare the performance of two models on the same dataset, set the second model using the set_compare_ai_platform_model method. One of our demo notebooks shows you how to use this method to compare tf.keras and Scikit Learn models deployed on Cloud AI Platform.
Understanding What-If Tool visualizations Click here for a full walkthrough of the features of the What-If Tool.
The initial view in the tool is the Datapoint Editor, which shows all examples in the provided dataset and their results from prediction through the model:
Click on any example in the main panel to see its details in the left panel. You can change anything about the datapoint and run it again through the model to see how the changes affect prediction. The main panel can be organized into custom visualizations (confusion matrices, scatter plots, histograms, and more) using the dropdown menus at the top. Click the partial dependence plot option in the left panel to see how changing each feature individually for a datapoint causes the model results to change, or click the “Show nearest counterfactual datapoint” toggle to compare the selected datapoint to the most similar datapoint that the model predicted a different outcome for.
The Performance + Fairness tab shows aggregate model results over the entire dataset:
Additionally, you can slice your dataset by features and compare performance across those slices, identifying subsets of data on which your model performs best or worst, which can be very helpful for ML fairness investigations.
Using What-If Tool from AI Platform Notebooks The WitWidget comes pre-installed in all TensorFlow instances of AI Platform Notebooks.
You can use it in exactly the same way as we’ve described above, by calling set_ai_platform_model to connect the What-If Tool to your deployed AI Platform models.
Start building Want to start connecting your own AI Platform models to the What-If Tool? Check out these demos and resources:
Demo notebooks: these work on Colab, Cloud AI Platform Notebooks, and Jupyter. If you’re running them from AI Platform Notebooks, it will work best if you use one of the TensorFlow instance types.
XGBoost playground example: connect the What-If Tool to an XGBoost mortgage model already deployed on Cloud AI Platform. No Cloud account is required to run this notebook.
End-to-end XGBoost example: train the XGBoost mortgage model described above on your own project, and use the What-If Tool to evaluate it.
What-If Tool: For a detailed walkthrough of all the What-If Tool features, check out their guide or the documentation.
We’re actively working on introducing more capabilities for model evaluation and understanding within the AI Platform to help you meaningfully interpret how your models make predictions, and build end user trust through model transparency. And if you use our new What-If Tool integration we’d love your feedback. Find us on Twitter at @SRobTweets and @bengiswex.
For the second consecutive year, Gartner has identified Google Cloud as a Leader in the 2019 Gartner Cloud Infrastructure as a Service Magic Quadrant. Enterprises rely on research firms like Gartner to help them evaluate and compare cloud providers, and we’re thrilled for the recognition.
Here at Google Cloud, our goal is to be the easiest cloud for enterprises to do business with. We’ve committed to dramatically extending the size of our sales and support teams, and have made meaningful changes to our contracting and discounting practices. Further, we’re working closely with key ISVs and service providers to ensure that running enterprise workloads on Google Cloud Platform (GCP) is a seamless, satisfying experience.
Customers also choose GCP for its differentiated technology. Here’s a sampling.
High-performance global scale that’s highly reliable At Google Cloud, we’ve worked hard to build infrastructure that organizations can count on, wherever they choose to deploy their workloads. In addition, Gartner calls out our innovative Customer Reliability Engineering team—engineers trained in Google’s rigorous Site Reliability Engineering (SRE) processes who teach customers how to run applications in a way that maximizes uptime, while still encouraging innovation.
Leading data analytics and machine learning Running applications is important, but you also need to make sense of the data they generate. Google Cloud has highly differentiated offerings in the realm of data analytics and machine learning. BigQuery, for instance, is our hyper-scalable, hosted serverless data warehouse that lets you query data with a familiar SQL-like interface, to meet all your enterprise data analytics needs. We’re also extending BigQuery with easy-to-use ML capabilities, so you can leverage the power of AI on existing data sets without having to hire a team of data science PhDs.
Open source for the enterprise Many Google Cloud IaaS offerings benefit from our innovation in the field of containers, networking and automation. While some customers choose GCP to build cloud-native applications, this year, we’ll bring open-source automation and scalability to the enterprise directly with Anthos, which takes the best of open source (Kubernetes, Istio, Knative) to help enterprises modernize any application, and run them wherever they see fit—in our cloud, on-premises, or even in third-party clouds.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner Magic Quadrant for Cloud Infrastructure as a Service, Worldwide, Raj Bala, Bob Gill, Dennis Smith, David Wright, 16 July 2019.
You’ve chosen G Suite for your business, and now it’s your job to get it up and running for you and your employees. It’s a big step, but one that will make it easier for employees to work together and go mobile. The step-by-step guide below will walk you through everything from setting up your account to training workers on the finer points of G Suite.
1) Sign in to your G Suite administrator account and set up your first users. This is where you can manage all of the G Suite services for your business, including mobile device management, data migration, setting password requirements, and much more. This guide walks you through the admin sign-in process.
After you sign in, you will need to set up your G Suite account by 1.) verifying you own your domain, 2.) adding user accounts for your employees, and 3.) when all employees have been added, setting up Gmail as your email client by updating your MX records. Don’t worry, none of these changes will affect your website, if you have one.
Insider tip: Changing MX records can take anywhere between five minutes and a couple of hours to take effect. In order to reduce disruptions, change your MX records at night or over the weekend when employees are less likely to be sending and receiving email.
2) Start a G Suite pilot program. Before you roll out G Suite to everyone, you may want to do a test run with a small group. That way you can collect helpful feedback about Gmail, experiment with migrating old emails and calendar events to G Suite, and configure G Suite for mobile devices. Your pilot group can test Gmail without changing their current addresses.
3) Spread the word about G Suite and start training. Workers are understandably nervous when businesses introduce new technology. Reassure them with helpful emails to make the changeover go smoothly. Think about providing details to them about what to expect when data migration happens, or send welcome emails to new users with helpful tips on getting started with G Suite—you can find sample emails to help you get started here.
In addition, consider setting up internal training resources tailored to the G Suite tools that your users rely on the most. For example, if your users use a particular app frequently, consider providing them with a specific how-to guide on how to use that app. This will save you with support questions in the long-term.
Insider tip: Once you have these support materials, consider making a quick, internal website for your team using Google Sites. You can add links from the G Suite Learning Center that relate to commonly-used tools, as well as house onboarding or training documents. Don’t forget to include contact info for IT team members who are available to help.
4) Migrate data such as mail, calendars, and contacts. This is a crucial part of the rollout, but Google has tools that can help make the migration go smoothly. You’ll be able to migrate data from other cloud services including Gmail, or from enterprise servers housing your data. Choose one of the data sources from this list to get started.
5) Set up G Suite on mobile devices. Securing endpoints like mobile devices is one of the best ways for businesses to keep data safe—and G Suite’s mobile management already covers more than 40 million devices. Basic device management is automatically enabled for your mobile devices that access G Suite. This means employees don’t have to install profiles on iOS and Android devices. Admins can also choose to opt for advanced mobile management from within the Admin Console to get even more security management controls to help them.
Got more questions about running G Suite like a pro? We have resourceson rolling out G Suite to employees, migrating and synchronizing data, and helping workers get familiar with G Suite. You can also check out our quick start guides that are customized to match the size of your company.
A fundamental piece of many applications is a database, and that’s true for many cloud-based solutions too. Running a database in the cloud is in many ways similar to running a database on-premises, but there are important differences—and advantages.
Our team—database solutions architects here at Google Cloud—works to help you understand every aspect of databases in the cloud: deploying, migrating, and managing. We want to help you choose the right way to run your database on Google Cloud Platform (GCP).
When you run a database on GCP, you can choose between managed services or running on infrastructure we manage for you. Managed services can remove some of the operational overhead required to operate a database, while running it yourself gives you full control over how your database is deployed. With both options, you get reliability, security, and elasticity built in, with the ability to get global connectivity using Google’s network.
Our team works continually to help users understand every aspect of databases in the cloud: deploying, migrating, and managing. Here are some examples of our recent solutions for using cloud databases, from deployment to monitoring.
Deploying IBM Db2
The first step for cloud-based databases is, of course, to get your database up and running. IBM Db2 is a common enterprise database, so we recently published a comprehensive solution document that describes how to deploy IBM Db2 on GCP: Deploying highly available IBM Db2 11.1 on Compute Engine with automatic client reroute and a network tiebreaker. The solution starts with setting up Compute Engine instances (VMs) to run Db2. As you can tell from the title, it goes well beyond the basics of deployment—it walks you through how to create a highly available deployment in a cluster with transaction replication and automated failover, as shown here:
And the solution doesn’t just stop when you’ve set everything up. The goal is high availability, so to make sure everything is working, author Ron Pantofaro shows you how to temporarily disable the primary cluster node. You can then verify that the database fails over properly and that the standby node takes over.
Migrating an existing database to GCP
In many cases, you aren’t deploying a database from scratch. Instead, you want to migrate an existing database to GCP. Just migrating a database from one platform to another can have its challenges. But what if you also want to change from a NoSQL database to a relational one?
In his solution Migrating from DynamoDB to Cloud Spanner, SA Sami Zuhuruddin recently tackled this interesting and challenging transition. He describes how to move your data from Amazon DynamoDB, which is a NoSQL database, to Cloud Spanner, which is a fully relational, fault-tolerant SQL database with transaction support.
When you read Sami’s solution, you’ll see why you’ll want to follow his expert guidance for this task. The process goes through a number of intermediate steps that include Amazon S3, Google Cloud Storage, AWS Lambda, Cloud Pub/Sub, and Cloud Dataflow before arriving at Cloud Spanner. Sami explains the data model on both sides of the migration, including keys, data types, and indexes. You’ll see which user permissions you need in order to perform each step. The solution walks you through the entire process, including verification at the end. Here’s a look at the architecture involved:
Backing up a database
It’s just as important in the cloud as it is on-premises to back up your databases. Two recent solutions discuss ways to do this.
In Using Microsoft SQL Server backups for point-in-time recovery on Compute Engine, SA Ron Pantofaro turns his hand from deployment to backup and shows you how to configure backup for a SQL Server instance that’s running on Compute Engine. You’ll see how to back up both the data and the database logs to Cloud Storage. He also describes how to restore a backup in case you ever need to do that (though we hope not). This isn’t the end of the job, though. From there, you’ll see how to schedule your backups and how to prune backups that you no longer need.
One of the benefits of running a database in GCP is that you can take advantage of services like Stackdriver to gather tracing information. In his community tutorial Client-side tracing of Cloud Memorystore for Redis workloads with OpenCensus, SA Karthi Thyagarajan discusses how to add tracing that lets you measure data-retrieval latency. This solution uses a data store consisting of Cloud Memorystore backed by Cloud Storage. As he says, this lets you “focus on the key aspects of client-side tracing without getting hung up on things like database deployments and related configuration.”
You can download the Java client app that Karthi created has a Java client app that you can get from GitHub, which already contains the logic for reading from the data store and generating trace output. After you’ve got the data store set up, you run the client app to read data. You can see some of the benefits of the instrumentation you’ve set up—you go to the Stackdriver console and visually compare the latencies of cached and non-cached reads:
More GCP database solutions
This covers just a few of the database-oriented solutions that our Solutions Architects team has produced. To find out more, check out the databases and migration entries in the GCP Solutions Gallery.
Hiring your business’s first employees is a big step—but it can also be a bit scary. There’s the time you spend on training and orientation, not to mention setting people up to be productive right away. On top of all that, you still need to run your business and keep customers happy.
If you’re worried about adding “human resources manager” to the many hats you wear, don’t be. From employee 1 to 1,000, G Suite’s easy-to-use tools can help you quickly hire and onboard new employees without adding too many more tasks to your already busy schedule.
Here’s your to-do list for onboarding new employees—once you’re done, find more onboarding tips here.
1. Create a business email address. You’ll want your new hires to start emailing right away—but using their work email accounts, not their personal ones. It’s important to avoid having employees work from their personal accounts since it will be challenging to recover files. It also makes it harder to keep track of who has access to your company’s information.
2. Share calendars. Of course, you’ll need to coordinate schedules with your employees. Instead of sending emails back and forth to book meetings or to log out-of-office times, add the employees to a shared Google Calendar. In a shared Google Calendar, you can keep track of the entire team’s schedule, not just an individual. Since calendar changes and additions are instantly displayed to all employees, it’s an easy way to make sure that you and your new hires are on the same page right from the start.
3. Reduce paperwork clutter by creating shared folders online. Keeping track of paper forms is a headache. Try moving routine forms and employee handbooks to a template in a digital document and storing those forms in a shared Drive folder for access by your new hires. Also, create online checklists in Google Keep or Docs so that your new team members can get up to speed on their jobs quickly. Your checklists could include orientation meetings for new employees or a list of forms to fill out for health coverage.
4. Create a training hub. Your first hires are hopefully just the initial step in building out a bigger (potentially global) team. To prepare for the future, begin building orientation materials and storing them in your shared Drive. For example, you can use Slides tocreate a slide presentation on policies for handling cash or how to process customer orders.
As you build out your orientation materials, think about creating a mini-onboarding portal in Google Sites to store them. Don’t worry, you don’t need to be a coding expert to build a website in Sites—you’ll find tips herefor getting started.
5. Chat about business even when you’re on the go. If you aren’t working in the same space as your new colleagues all day—maybe you both work from home, or you’re on the road a lot—chat and video meetings can help you quickly solve problems. It’s also a great way to keep your Gmail from filling up with low-priority emails.
You can set up Hangouts Chat on desktop computers, tablets, and phones so you’re always ready to send and receive messages. It’s also possible to search previous conversations for information, and start video meetings directly from chats.
If you’re considering adding more employees, you may be ready to take your human resources up a notch. Check out this article on the G Suite Learning Center for advice on how to create job descriptions, keep track of candidates, and interview by video.
System administrators are frequently asked to assess risk when moving their applications to the cloud. One common concern is the platform’s inherent presence on the internet, and how to properly secure cloud-based virtual machines and services that are now exposed. In Google Cloud, you can configure VMs and APIs so they’re not connected to the public internet but still accessible to system administrators. Here’s how.
Use Compute Engine’s No External IP Org policy
The first thing you can do to protect VMs it to configure a policy that disallows VMs from obtaining an external IP. From the admin console, click Security and select Organization Policies.
Note that this policy is not retroactive, so if you already have machines with external IP addresses, this policy does not remove them. Also be aware that while the default VPC has firewall rules to allow SSH / RDP, without an external IP, these are only accessible from the internal network.
Use Cloud Identity-Aware Proxy
Next, you need to allow developers to access these machines. Traditionally, you configure a VPN client to connect to the VPC. In Google Cloud, there’s a better way: you can use Cloud Identity-Aware Proxy (IAP) to connect to the machines. To show you how, we’ll follow this guide.
From the admin console, click Security then select Identity-Aware Proxy.
If you haven’t used Cloud IAP before, you’ll need to configure the oAuth screen:
Configure the consent screen to only allow internal users in your domain, and click Save.
Next, you need to define users who are allowed to use Cloud IAP to connect remotely. Add a user to the “IAP-secured Tunnel User” role on the resource you’d like to connect to.
Then, connect to the machine via the ssh button in the web UI or gcloud.
When using the web UI, notice the URL parameter useAdminProxy=true.
Tip: If you don’t have gcloud installed locally, you can also use Cloud Shell:
You should now be connected! You can verify that you don’t have internet connectivity by attempting to ping out. 8.8.8.8 (Google’s Honest DNS) is a good address to try this with.
Controlling access with VPC Service Controls
Most GCP developers will still want access to Google Cloud APIs. You can give them access to Google Cloud APIs while restricting them to resources that are present in the project by using VPC Service Controls.
First, enable private Google API access on the VPC network where your VM is located (in this example, US-West1.) Select the VPC network in the region where your virtual machines are located. Select the subnet, and click Edit. Enable Private Google access by selecting “Private Google Access” and click Save.
Once you’ve enabled private access, gcloud commands from the VM will work.
Now use VPC Service Controls to define where you’d like those API requests to be allowed. Navigate to the ORG node for your domain and select VPC Service Controls from the Security tab. Select New Perimeter. Add your project and the APIs you’d like to protect to the “New VPC Service Perimeter” and click Save.
Now that you’ve gone through all these steps, your VMs should be configured so they are only accessible via Identity-Aware Proxy, and only have access to the local network and Google APIs that are part of the project to which they belong.
As you can see, using Google Cloud tools like Cloud IAP and VPC Service Controls can help you insulate projects from the public internet, reducing risk and fears about moving apps and infrastructure to the cloud. To learn about more security capabilities and features, visit cloud.google.com/security.
Editor’s note: Today’s post is by Mike Francomb, Senior Vice President of Technology, RecruitMiliary and a U.S. Army Veteran. RecruitMilitary is a wholly owned subsidiary of Bradley-Morris, Inc. (BMI), the largest military-focused recruiting company in the United States. RecruitMilitary uses Google Cloud Talent Solution to power its job search experience and connect more organizations with veteran talent.
For seven years, I served in the U.S. Army as a Field Artillery Officer, Military Occupation Code 13A. My time in service included a deployment to Operation Desert Shield / Desert Storm with the 24th Infantry Division out of Fort Stewart, GA, and a variety of front line artillery leadership roles, serving as a logistics officer for my unit and as an instructor teaching new officers how to be professional artillerymen. My day-to-day entailed leading teams of highly trained soldiers and managing logistics and materials to help those soldiers perform at a high level in stressful, fast-paced environments. It was my job to ensure we were ready to handle any circumstance.
The hardest part about transitioning out of the Army in May 1996 as a highly trained artillery veteran was the fact that, though I felt prepared for any challenge ahead, I wasn’t sure I was making the right choice. I made a common mistake of transitioning veterans, I jumped right into an entrepreneurial venture. Looking back, I wish I’d had access to resources that displayed career options that would help translate my skills for the corporate world, it would have helped me be better prepared and know what my options were. I wasn’t ready to jump from the Army into running a business, and it was a long two years.
Though my first job out of the Army was challenging, it taught me that I loved the start-up environment, and I joined RecruitMilitary in October 1998 when it was five months old. For the past 21 years, I have been fortunate enough to play an important role in helping RecruitMilitary grow to what it is today, the industry leader in connecting military veterans with organizations.
RecruitMilitary connects organizations with veteran talent through over 30 products and services, all of which are fueled by our job board. Our job board, with over 1,400,000 members, is core to our business. In fact, if we don’t have an active and growing job board population, we don’t have the supply of veteran talent we need to deliver to our clients across our suite of services.
With veteran unemployment at a 50-year low, it became increasingly challenging for RecruitMilitary to grow our veteran job seeker database and keep those veterans actively applying to client jobs. Being a data-driven company, we saw our existing search functionality was no longer producing the desired results for clients and began to receive client feedback about decreased candidate activity.
It was clear to us that we needed to begin adopting machine learning and more advanced search capabilities into our products and operations. The HR Tech space is shifting that way fast, and we want to be at the forefront. As we researched paths to take and learned of Google’s operating philosophy leading with AI, and that they were developing a tool for veteran job search, it made a lot of sense to go with a leader.
When Grow with Google announced its commitment to support veterans, we learned that we could add their military occupation code (MOS) translation feature to our job board through Cloud Talent Solution. This feature lets transitioning service members enter their military occupation codes (MOS, AFSC, NEC, or rating) directly into our search bar to see relevant civilian jobs available at client companies. We’re also using Cloud Talent Solution’s remote work functionality to provide an improved job search experience that allows our customers to make remote work opportunities in the U.S. more discoverable on their career sites. We’re excited about this feature, as it enhances our ability to deliver meaningful jobs to important members of our military community, military spouses, and veterans with limited mobility.
The results of Cloud Talent Solution compared to our previous search are tremendous. Our job seekers are getting a much better experience, and to us that means more veterans are connected to jobs with our clients. We know this because our number of daily job applications has grown by 78 percent. And knowing that we now have a tool that’s going to learn and get better as more of our job seekers use it means that we will reap benefit for work done over time, and so will our clients and veterans who use our job board. That’s tremendous ROI to receive for a lean development staff.
These are just a few of the types of tools I wish I’d had access to when I was considering my transition in 1996. With the help of technology and resources, like those from RecruitMilitary and Grow with Google, people in the military community, including veterans like myself, can prepare for and build meaningful careers.
Today, IT organizations want to move fast, deploy software efficiently, and scale big. Kubernetes, containers, and Google Kubernetes Engine (GKE) can help you do that—and we can help you get started with learning these technologies with our newest training path, the Architecting with Google Kubernetes Engine Specialization.
In this specialization, you’ll learn all about Kubernetes, the open-source, vendor-neutral system for orchestrating workloads that are packaged in containers. You’ll gain an understanding for how you can run Kubernetes, and deploy production solutions on it, usingGoogle Cloud Platform (GCP). We’ll also deep dive into our GKE managed service that gives you access to Google’s advanced load-balancing technologies, its worldwide network, and GCP’s range of data and machine-learning managed services. If you’re already familiar with working in a virtual-machine-based environment, this specialization will present familiar architecture principles, but in a GKE environment. You’ll also learn how to configure your GKE environment; build, schedule, load-balance, and monitor workloads; manage access control and security; and give your applications persistent storage.
This specialization is delivered as a combination of lectures and hands-on labs to help you master your skills. When you finish each course, you will receive a certificate that you can share with your professional network and employers.
The Architecting with GKE Specialization consists of four courses:
GCP Fundamentals: Core Infrastructure – Learn about important concepts and terminologies for working with GCP.
Architecting with GKE: Foundations – Master the foundations of architecting with GKE by reviewing the layout and principles of GCP, followed by an introduction to creating and managing software containers and to the architecture of Kubernetes.
Architecting with GKE: Workloads – Learn about how to perform Kubernetes operations, create and manage deployments, use GKE networking tools; and how to give your Kubernetes workloads persistent storage.
Architecting with GKE: Production – The last course covers Kubernetes and GKE security, logging and monitoring, and using GCP managed storage and database services from within GKE.
Want to learn more? Join us July 26th at 9:00 am PST for a special webinar, Architecting with Google Kubernetes Engine: Get started on your Anthos journey. In this webinar you will learn how to enable a hybrid cloud strategy with Kubernetes and participate in a free hands-on lab on how to configure persistent storage for GKE. Just for attending the webinar, we’ll give you one month free access to the GKE Specialization. Register for the webinar today.
Google Cloud has some great tools for software developers. Cloud Source Repositories and Stackdriver Debugger are used daily by thousands of developers who value Cloud Source Repositories’ excellent code search and Debugger’s ability to quickly and safely find errors in production services.
But Debugger isn’t a full-fledged code browser, and isn’t tightly integrated with all the most common developer environments. The good news is that these tools are coming together! Starting today, you can debug your production services directly in Cloud Source Repositories, for every service where Stackdriver Debugger is enabled.
What’s new in Cloud Source Repositories
This integration brings two pieces of functionality to Cloud Source Repositories: support for snapshots, and logpoints.
Snapshots Snapshots are point-in-time images of your application’s local variables and stack that are triggered when a code condition is met. Think of snapshots as breakpoints that don’t halt execution. To create one, simply click on a line number as you would with a traditional debugger, and the snapshot will activate the next time that one of your instances executes the selected line. When this happens, you’ll see the local variables captured during the snapshot and the complete call stack—without halting the application or impacting its state and ongoing operations!
You can navigate and view local variables in this snapshot from each frame in the stack, just as with any other debugger. You also have full access to conditions and expressions, and there are safeguards in place to protect against accidental changes to your application’s state.
Logpoints Logpoints allow you to dynamically insert log statements into your running services without redeploying them. Each logpoint operates just like a log statement that you write into your code normally: you can add free text, reference variables, and set the conditions for the log to be saved. Logpoints are written to your standard output path, meaning that you can use them with any logging backend, not just Stackdriver Logging.
Creating a logpoint is a lot like creating a snapshot: simply click on the line number of the line where you wish to set it, and you’re done.
Upon adding a logpoint to your application, it’s pushed out to all instances of the selected service. Logpoints last for 24 hours or until the service is redeployed, whichever comes first. Logpoints have the same performance impact as any other log statement that exists normally in your source code.
Getting started
To use Cloud Source Repositories’ production debugging capabilities, you must first enable your Google Cloud Platform projects for Stackdriver Debugger. You can learn more about these setup steps in the Stackdriver Debugger documentation.
Once this is complete, navigate to the source code you wish to debug in Cloud Source Repositories, then select ‘Debug application’. Today this works best with code stored in Cloud Source Repositories or that is mirrored from supported third-party sources including GitHub, Bitbucket, and GitLab. Once you’ve selected your application you can start placing snapshots and logpoints in your code by clicking on the line numbers in the left gutter.
Production debugging done right
Being able to debug code that’s running in production is a critical capability—and being able to do so from a full-featured code browser is even better! Now, through bringing production debugging to Cloud Source Repositories, you can track down hard-to-find problems deep in your code, while being able to do things like continually sync code from a variety of different sources, cross-reference classes, look at blame layers, view change history, and search by class name, method name, etc. To learn more, check out this getting started guide.
Russell Wolf, Product Manager, also contributed to this blog post.
As cryptocurrencies have gotten more popular, we’ve seen the birth of thousands of new currencies and, in parallel, just as many platforms to use them on. One prominent example at the forefront of cryptocurrencies is Blockchain, which has helped 39 million cross-platform wallet users in 140 countries worldwide access the crypto ecosystem.
Blockchain, a Google Cloud customer, was initially focused on creating tools to understand and use Bitcoin, but the company has since expanded to other cryptocurrencies like Ethereum, Bitcoin Cash, Stellar Lumens and the Paxos Standard. Now, millions of individuals rely on the Blockchain Wallet to secure and use the world’s leading cryptocurrencies.
Needless to say, with the size and geographic sprawl of its user base, managing these datasets is no easy feat.
Meeting user needs while growing fast
Since the company’s inception, Blockchain has used Google Cloud Platform (GCP), adding services wherever the team saw opportunities to meet its evolving needs. While Blockchain maintains some of its own hardware and data centers, it wanted to evolve its approach to managing infrastructure to enhance the security, reliability, and accuracy of information platforms.
Blockchain’s flagship products, Blockchain Wallet and Blockchain Explorer, require complicated calculations on hard-to-access data across the massive, decentralized ledgers that underpin cryptocurrency networks. Accessing that data requires complex domain knowledge, technical infrastructure, and development effort, not to mention time to process the entire data chain. This became a major undertaking that required significant in-house IT resources and overhead.
To manage these challenges and enhance the user experience across all products and platforms, Blockchain began running infrastructure on Compute Engine instances. Blockchain also chose Cloud Spanner as its database service of choice because this database server could scale fast (with no downtime), and provide high-availability with low operational overhead. Cloud SQL, Stackdriver, and identity management products also make up Blockchain’s cloud infrastructure.
Securing user financial data
With millions of users across the globe relying on Blockchain for information about and access to their funds, it’s no surprise that one of its core values is “Sanctify Security.”
“Security is our number one priority,” says Lewis Tuff, head of platform engineering at Blockchain. “Google Cloud goes above and beyond to help protect data, infrastructure, and services from external threats. GCP makes it easy to get the basics of security right. Cloud Identity Access Management (Cloud IAM) and VPC firewall allow Blockchain to lock down access to resources according to the least privilege principle and implement defense in depth within our environment. Leveraging Stackdriver’s logging and monitoring capability enable us to be alerted to any unusual activities in real time.”
Blockchain also uses Google’s Cloud Identity-Aware Proxy (Cloud IAP) to control cloud app access through user identity verification and context awareness. It also uses Cloud Key Management Service (Cloud KMS), integrated with Cloud IAP, to manage cryptographic keys for a comprehensive approach.
“So many companies would benefit from Cloud IAP,” Tuff adds. “It’s really easy to authenticate and activate applications based on G Suite accounts. That’s huge for us because we have a number of internal and back-office applications that can now be managed through granular, role-based access rights.”
Scaling on demand to match large volumes of data
When it came time for Blockchain to expand its Explorer offering to include the Ethereum network, it turned to Cloud Spanner, GCP’s strongly consistent, high-availability (up to 99.999% SLA) database service, to accelerate deployment and keep pace as data volumes grew rapidly, while maintaining reliability. Cloud Spanner’s on-demand scalability let Blockchain cut its operational overhead—the company has achieved savings of 30% by replacing its previous database layer with Cloud Spanner.
With Cloud Spanner, Blockchain knew it could start small and not have to worry about growth of datasets as its service grew. One example of the scaling power the company has found using GCP is the import and export functionality, which allows them to perform a full restoration of the database in nine hours, instead of one week. That’s essential for the company’s rapid development work, and eliminates the need to ingest the whole chain from the genesis block in each environment.
“That kind of flexibility is really powerful,” says Tuff. “It means we can run our dataset through our environments very easily. We can add nodes as and when required, with high availability and strong consistency across a scalable, distributed database.”
Moving forward, Blockchain’s team is strategically preparing for the future of an emerging market, using GCP services to help execute on initiatives across product and engineering.
Building out with managed services
In addition to expanding its use of Cloud Spanner as a managed database, and Stackdriver Monitoring for logging metrics and analysis, Blockchain is building more microservices with GCP managed services. “It makes sense to see what we can do with GCP products, instead of spinning up our own VMs and managing the whole backup and failover strategy,” says Tuff.
He added, “We’re a fast-moving company, so our relationship with Google Cloud has been invaluable. When we needed advice, the Cloud Spanner team talked through our ideas so we could build the right architecture. The team is experienced, knowledgeable, and dedicated to finding the right architecture for your use case. When you come to Google Cloud with a challenge, the team puts all its talent behind finding the best solution.”
Posted by Marisa Pareti, Rubi Martinez & Jessica Earley-Cha
In celebration of International Women’s Day, Women Techmakers hosted its sixth annual summit series to acknowledge and celebrate women in the tech industry, and to create a space for attendees to build community, hear from industry leaders, and learn new skills. The series featured 19 summits and 305 meetups across 87 countries.
This year, Women Techmakers partnered with the Actions on Google team to host technical workshops at these events so attendees could learn the fundamental concepts to develop Actions for the Google Assistant.Together, we created hundreds of new Actions for the Assistant. Check out some of the highlights of this year’s summit in the video below:
Technical Workshop Details
If you couldn’t attend any of our meetups this past year, we’ll cover our technical workshops now so you can start building for the Assistant from home. The technical workshop kicked off by introducing Actions on Google — the platform that enables developers to build Actions for the Google Assistant. Participants got hands-on experience building their first Action with the following features:
Users can start a conversation by explicitly calling the Action by name, which then responds with a greeting message.
Once in conversation, users are prompted to provide their favorite color. The Action parses the user’s input to extract the information it needs (namely, the color parameter).
If a color is provided, the Action processes the color parameter to auto-generate a “lucky number” to send back to the user and the conversation ends.
If no color is provided, the Action sends the user additional prompts until the parameter is extracted.
Users can explicitly leave the conversation at any time.
During Codelab level 1, participants learned how to parse the user’s input by using Dialogflow, a tool that uses Machine Learning and acted as their Natural Language Processor (NLP). Dialogflow processes what the user says and extracts important information from that input to identify how to fulfill the user’s request. Participants configured Dialogflow and connected it to their code’s back-end using Dialogflow’s inline editor. In the editor, participants added their code and tested their Action in the Action Simulator.
In Codelab level 2, participants continued building on their Action, adding features such as:
Supports deep links to directly launch the user into certain points of dialog
Uses utilities provided by the Actions on Google platform to fetch the user’s name and address them personally
Responds with follow-up questions to further the conversation
Presents users with a rich visual response complete with sound effects
You can learn more about developing your own Actions here. To support developers’ efforts in building great Actions for the Google Assistant, the team also has a developer community program.
Alex Eremia, a workshop attendee, reflected, “I think voice applications will have a huge impact on society both today and in the future. It will become a natural way we interact with the items around us.”
From keynotes, fireside chats, and interactive workshops, the Women Techmakers summit attendees enjoyed a mixture of technical and inspirational content. If you’re interested in learning more and getting involved, follow us WTM on twitter, check out our website and sign up to become a member.
Posted by Marisa Pareti, Rubi Martinez & Jessica Earley-Cha 
