Machine Learning for .NET developers

Democratizing machine learning continues, this time for .NET. Google Cloud is committed to supporting developers getting their .NET workloads up and running on the GCP. Enroll in the Developing Data and Machine Learning Apps with C# and you’ll get 35 Qwiklabs credits, good towards labs in the new Quest (free of charge, no CC required).

Each lab demonstrates how to hook up your data to Google’s ML tools with just a few simple commands. You even get app templates you can re-use when you work on your own projects.

  • Using BigQuery with C# (60 mins): To  or not to , that is the question. Use BigQuery to analyze Shakespeare, a GitHub dataset and data you upload yourself (could be anything).
  • Using the Natural Language API with C# (45 mins): Analyze how awesome Yukihiro Matsumoto is, with documentSentiment. What did you get? The overall sentiment analysis consists of two fields: sentiment and magnitude. Sentiment ranges between -1.0 (negative) and 1.0 (positive). Magnitude indicates the overall strength of emotion (both positive and negative) within the given text, between 0.0 and +inf. Pop quiz: who remembers how to diagram a sentence? Now you can #makegoogledoit, one more talent of the Natural Language API.
  • Using the Speech-to-Text API with C# (45 mins): How many languages do you speak? The Google Cloud Speech-to-Text API has 120 different languages and variants. Even French or a Brooklyn accent. For extra credit, see if the API can transcribe a clip of your speech when you take the lab. Check it out on TechCrunch (:
  • Using the Video Intelligence API with C# (45 mins): You’ll work with a shot-change detection example in this lab. What other types of changes would you want to identify? Maybe a model that detects activity in wildlife monitoring cameras or security footage…
  • Using the Vision API with C# (45 mins): What do otter crossing signs have in common with the Eiffel Tower? The Google Cloud Vision API can recognize them both. Learn how to protect wildlife and organize your vacation photos with a lab. Then apply the skills you learned next time you build a license plate recognition app, or rank the most popular landmarks #teampixel is sharing.

  • Using the Translation API with C# (45 mins): How do you say “hello world” in Turkish? I don’t know – but we can use the Translation API to do it for us. Text is translated using the Neural Machine Translation (NMT) model. If the NMT model is not supported for the requested language translation pair, then the Phrase-Based Machine Translation (PBMT) model is used. And “hello world” in Turkish? Selam Dünya!
  • Cloud Spanner: Create a Gaming Leaderboard with C# (60 mins): Ready player one – wait, who won the last round? Learn to create a leaderboard with C# – this one is the most advanced lab in the Quest so prepare to be challenged!

Hurry, click here to enroll by the weekend and get 35 credits to help you get started. Good luck!

Terraform training on GCP

Did you know that there are over 3000 jobs on LinkedIn that require Terraform expertise? Terraform is the first multi-cloud immutable infrastructure tool for developing, changing and versioning infrastructure safely and efficiently. Terraform can also manage existing service providers and custom in-house solutions.

Qwiklabs can help you learn how to take advantage of what Terraform has to offer. Enroll in the Managing Cloud Infrastructure with Terraform Quest by Thursday, August 30th and you’ll get 40 credits. Use these credits to launch a range of configurations, from simple servers to full load-balanced applications.

If you’re new to GCP, consider tackling the Baseline: Deploy & Develop Quest first. Are you an experienced Google Cloud user? Here’s how the Managing Cloud Infrastructure with Terraform Quest can help you get more comfortable with Terraform:

     After creating a Kubernetes engine cluster, you’ll extract the Kubernetes        engine master IP and network tag name using the gcloud command-line        tool:

And then deploy the NAT gateway instance using Terraform commands.

  • Modular Load Balancing with Terraform – Regional Load Balancer: GCP uses forwarding rules to construct a load balancer across multiple regions and instance groups. Since these forwarding rules are combined with backend services, target pools, URL maps and target proxies, Terraform uses modules to simplify the provisioning of load balancers.

In this lab, you’ll work with different modules to create various load balancers. For example, in the terraform-google-lb-http (global HTTP(S) forwarding rule) module, you’ll provide a reference to the managed instance group and certificates for SSL termination. Then the module creates a global HTTP load balancer for multi-regional content.

Do you know what happens behind the scenes? The module creates the http backend service, URL map, HTTP(S) target proxy, and the global http forwarding rule to route traffic based on HTTP paths to healthy instances:

You’ll then unseal the Vault after getting the decrypted keys from Cloud Storage:

Are your results similar to the example output? Share your results @Qwiklabs.

    • Cloud SQL with Terraform: In this lab you’ll create Cloud SQL instances with Terraform, set up the Cloud SQL Proxy and test the database connection with both MySQL and PostgreSQL clients.

Enroll now to either build career opportunities or to boost your team’s efficiency.

Cloud certification: Hands-on practice is just a lab away

Infrastructure Architect is the fourth highest-paying job in the tech industry. Add the Cloud Architecture Quest badge to your resume and set your sights on the Cloud ACE certification (and maybe one of the 1,097 open architect jobs). The Quest maps to the topics covered in the new ACE certification exam guide and puts your GCP knowledge to test. Here’s how:

  • In the Cloud IAM: Qwik Start lab you’ll assign a role to a second user and remove assigned roles associated with Identity and Access Management (IAM). This maps to section 5 from the exam guide, Configuring Access and Security.

Explore the IAM console in the lab and you’ll see:

    • Practice creating Google Compute Engine VM instance in the Stackdriver: Qwik Start lab. It will help you with section 3.1 from the exam guide, deploying and implementing Compute Engine resources.
    • Tackle the Orchestrating the Cloud with Kubernetes lab to provision a Kubernetes cluster and deploy and manage Docker containers using kubectl. This helps you practice for section 3.2, Deploying and implementing Kubernetes Engine resources.

    Here’s a brain teaser! Nginx container is running, expose it using kubectl      expose command in the lab.

What happened? Share your results @Qwiklabs

  • Conquer the Deployment Manager – Full Production lab to set up black box monitoring with Stackdriver Dashboard and establish uptime check alerts to trigger incident responses. This maps to section 3.7 from the exam  guide, Deploying an Application using Deployment Manager.

Why wait? Enroll in the Cloud Architecture Quest by Friday, August 24th and get a 1-month pass to help you prepare for the Associate Cloud Engineer Certification exam.

Take AutoML Vision for a test drive

What kind of cloud is this?

I don’t know either. Can you think of other types of images that would be useful to classify? For example, if you have an online tire shop, wouldn’t it be cool if your customers could upload a photo of their current tire to find a matching replacement?

AutoML can help your tire shop decrease the number of returns and increase customer delight. And it’s easier than you think – learn how to take advantage of this powerful new tool with a new lab, AutoML Vision API.

Here’s some of what you’ll do:

  • You’ll be prompted to log into the AutoML UI. Open the link in a new tab to make it easy to hop between AutoML and your lab manual. Then, you’ll log into AutoML with your Qwiklabs-provided credentials.
  • In the AutoML UI, you will see a button for “Billing”. You do not need to worry about billing for this lab, as Qwiklabs covers the cost of this lab for you.
  • When you load data into your storage bucket, you’ll set your project name with one of the first commands you run. Find your project name on the left of your lab manual, just below your GCP credentials:

AutoML is currently in beta, so we expect to see constant changes to the user interface. Send any questions to Support@Qwiklabs.com and we’ll be happy to help. Plus you’ll help us keep the lab up to date as the product continues to improve!

Curious about what type of clouds were spotted during our trip to Muir Woods? Here are our model’s predictions:

The model is 99% certain these are cumulus clouds! Prediction certainty and accuracy would be better if we trained our model with more than 20 images per category. When you’re helping customers order tires, you will probably want to use 100+ photos per category.

Happy lab-taking!

 

Dig deep into BigQuery Machine Learning with Google Cloud

You don’t need a B.S. in Computer Science to take advantage of powerful GCP data analysis tools. Google announced fully managed service at Next’18, BigQuery Machine Learning (BQML). BQML allows you to:

  • Create, train, evaluate, predict and deploy machine learning models with minimal coding
  • Use SQL directly in the database

Get started with BQML by taking the Predict Visitor Purchases with a Classification Model in BQML lab today and get a 30 day pass (free of charge, no CC required) to help you take advantage of this shiny new toy — I mean, seriously, an impactful data analysis tool!

Here’s some of what you’ll do in the lab:

  • Explore Ecommerce data & run the Query to find out what percentage made a purchase from website visitors:
  • Run the Query to find out your top 5 selling products: 
  • Preview the demo dataset to find useful features a machine learning model uses to understand if a first time visitor will return to make a purchase & dig deeper to know the risks of only using these fields for your classification model:
  • Evaluate classification model to maximize the True Positive Rate (predict that returning users will make a purchase) & visualize a ROC (Receiver Operating Characteristic) curve to maximize the area under the curve:Take the lab  and get your 30 day pass to be one step closer to earning the Data Engineering Quest badge.

Google Cloud training: 33 new labs

Big things are happening in Google Cloud. BigQuery ML, for example, just announced at Next ‘18. Even if you weren’t there, you can still take advantage of 33+ new labs released at the conference. And if you haven’t yet seen the new BigQuery interface, you can check it out with a lab.

Here’s what’s new on Qwiklabs:

  1. Predict Visitor Purchases with a Classification Model in BQML: Use the latest and greatest technology to answer the top question in online retail. Click here to take the lab free of charge, first 25 readers only.
    Google Cloud BQML lab
  2. Classify Images of Clouds in the Cloud with AutoML Vision: AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Take AutoML on a test drive with this lab.
  3. Managing Cloud Infrastructure with Terraform: #MakeGoogleDoIt – launch your cloud resources, that is. Learn how Terraform can boost your team’s efficiency, by creating configuration files that can be shared, treated as code, edited, reviewed, and versioned.
    Terraform Google Cloud Training
  4. Kubernetes Solutions: Work hard, play harder. Use Kubernetes to run dedicated gaming servers, plus 7 more advanced Kubernetes use cases in this Quest.
    Kubernetes Google Cloud Training
  5. Network Performance and Optimization: If you’re the one they call when the network is down, these labs are for you. Learn how GCP can help you sleep at night with better network speed, performance, and reliability.
    Networking Google Cloud Training
  6. Challenge Quest: Your company is ready to launch a brand new website, but the person who built the new site left the company before they could deploy it. Your challenge is to deploy the site in the public cloud. This and six other scenarios make up the Challenge Quest, to test your skills in a simulated crisis. Good luck!
    Challenge lab: Google Cloud Training

If you have an Advantage subscription, all of this new content is included in your subscription, no extra charge. Not a subscriber yet? Use promo code NEW33 for 33% off your first month. Promo code is valid through August 1o (Friday).

Good luck on your next Quest!

Get ready for Google Cloud Next ‘18

Are you ready for Next week? Join Google Cloud for the event of the year. Grow your network, grow your skills, and learn about the growth of Google Cloud – what’s happening now, and what’s next. And even if you’re not in San Francisco, join a Next event near you. Find one here.

Get a head start today by familiarizing yourself with key cloud concepts. Cloud training is available online, on-demand and on the real cloud console (not a video, lecture, or slideshow). Check out these hands-on labs, and follow any of the links below in the next 24 hours and you’ll get 25 Qwiklabs credits free of charge to help you start your Google Cloud training journey:

Complete a Quest and you’ll earn a badge. A badge shows your “flight time” with Google Cloud, and differentiates you as someone with hands-on GCP experience. Click this link → log into Qwiklabs → you will get 25 Qwiklabs credits to help you tackle your next Quest. Hurry, link expires in 24 hours.

See you next week!

The independence of upskilling

The U.S. will be celebrating its 242nd year of independence on July 4th. Set your own “independence” day – learn new skills and take your career where *you* want to go. Enroll in any of these Quests by Monday, July 8th and get a 30 day pass to earn these badges (free of charge, no credit card required). What skills can you add to your resume? 

  1. Baseline: Infrastructure Quest: Learn to use Cloud Storage, deploy a containerized application with Google Kubernetes Engine, build a Docker image, create and deploy a Cloud Function, publish and consume messages, create a Cloud IoT Core device registry and use Cloud Spanner. Here’s the badge you’ll earn:

  1. GCP Essential Quest: This Quest has the best of both worlds! While it helps you get more comfortable with the Google Cloud, like spinning up a virtual machine, it also challenges you to configure key infrastructure tools through working with more advanced Stackdriver and Kubernetes concepts. Tackle the Quest to earn this badge:

  1. Application Development Quest: Think you’re a Java Jedi or a Python powerhouse? Well we’ve got challenging Quests for you! In these two Quests, you’ll either put your Java or Python skills to test by developing cloud-based applications using GCP services such as App Engine, Cloud Datastore and Cloud Spanner. Then integrate these services and deploy to an App Engine and a Kubernetes Engine. Are you excited to earn these badges?

 

                                                           

 

 

4. Scaling Your Infrastructure Quest: Not for beginners! If you’re ready for an expert level Quest, this one’s for you. Learn how to: set up multiple NAT gateways, deploy an autoscaling Compute Engine, customize Stackdriver logging, set up Jenkins on Google Kubernetes Engine to help orchestrate your software delivery pipeline and use Spinnaker to continuously deploy the application when changes are made. You’ll earn this badge:

Celebrate your own independence day with us. Enroll by Monday, July 8th, get a 30 day pass (free of charge, no credit card required), and take charge of your career!

Dig deep into Machine Learning with Google Cloud: Part 2

If you haven’t enrolled in the machine learning Quests mentioned in this blog, you’re missing out! One last chance for free labs, follow any of the Quest links in this post and get a 30 day pass to finish these Quests. Hurry, offer expires Thursday, June 28 (free of charge, no CC required).

Tackle any of the Quests from the roadmap below to become a ML master. If you’re a beginner… begin at the beginning 🙂

Earn a machine learning Quest badge or two, get practice with real-world scenarios, and make the most out of your time at Next ‘18 with some ML rockstars. Who might you meet at Next ’18?

Fei Fei Li is a Chief Scientist of Cloud AI and ML at Google Cloud. She works in the areas of computer vision and cognitive neuroscience. Her publications include Scaling Human-Object Interaction Recognition through Zero-Shot Learning  and Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks. Get hands-on practice with Cloud AI at scale with the Machine Learning APIs Quest.

Or you might run into Valliappa Lakshmanan. Lak is on a mission to democratize machine learning so anyone can do it using Google’s infrastructure. Check out his latest book, Data Science on the Google Cloud Platform, explaining how to apply statistical and machine learning methods to real-world problems. And try it for yourself with a lab from the Scientific Data Processing Quest!

Take some labs ahead of time, gain a little experience, and ask better questions at the ML sessions offered at the Next ’18! Check out these sessions:

Can’t get enough of machine learning? When you register for Next ‘18, you will have the option to add some of these ML boot camps:

    • End-to-End Machine Learning with TensorFlow on GCP: You’ll go through the process of building a complete machine learning pipeline, covering ingest, exploration, training, evaluation, deployment and prediction.
    • Building and updating a Machine Learning Model on Edge Network using Cloud ML: You’ll learn how to process and store IoT or other data using Cloud Pub/Sub, Cloud Dataflow, Cloud Storage, and Google BigQuery.
    • Building Chatbots with Machine Learning: You’ll use Dialogflow and Cloud Natural Language API to rapidly convert an HR manual document into a fully functional and conversational chatbot. You’ll also add text and voice interactions to the chatbot and secure and scale it for production.

If you can’t make it to Next in San Francisco, take classes worldwide. Find one here.

To make the most of your time at Next ’18, enroll in these ML Quests by Thursday, June 28 (free of charge, no CC required).

Dig deep into Machine Learning with Google Cloud

Fun fact: there are 1,829 open positions for machine learning engineers on LinkedIn. Do you want to become a machine learning engineer? It’s easier than you might think, and we can help with a Quest or two.

Tackle any of the Quests from the roadmap below and become a ML expert:

1. Enroll in Baseline: Data, ML & AI Quest – If you’re new to ML, it’s a perfect Quest! You’ll work with big data, machine learning and artificial intelligence services in Google Cloud, like Dataproc and Bigtable.  Enroll now and get a free 30 day pass to earn this badge, plus as many others as you can (free of charge, no CC required). Hurry, link expires Thursday, June 21. 

 

2. The Machine Learning APIs Quest: This is the next step in your ML journey. You’ll capture text strings from images, recognize characters translate text into other languages using Cloud Vision API, Natural Language API and Translation API. You’ll even build a responsive chat bot using Google Cloud Dialogflow and train & deploy a TensorFlow model to Cloud ML Engine for serving (prediction).

 3. Enroll in Scientific Data Processing: Ready for a challenge? Tackle this Quest next. You’ll explore public domain scientific data sets, and learn to manipulate and transform data using the power of Google’s cloud infrastructure.

Can’t get enough of machine learning? Want to know what’s Next?  Stay tuned for Part 2!