The business value of AI is projected to reach $1.2 trillion this year according to Gartner. Yet 40% of enterprise companies are not adopting AI. Why not?
3 simple reasons:
- Data Scientists are hard to find and hire. Want to become one? There’s a lab for that. Enroll in the Data Science on GCP quest by Friday, October 12th and you’ll get 40 credits (free of charge, no CC required). The labs in this quest are derived from the book Data Science on Google Cloud Platform.
- Data analysts with SQL skills (and other programming languages) are also rare. Get practice running SQL queries in the BigQuery console in the Introduction to SQL for BigQuery and Cloud SQL lab. Then experiment with SQL and ML in the Ingest data into the Cloud Using Google App Engine lab. You’ll run Python scripts to download, automatically fetch, and clean data using Google App Engine. You’ll also create a new application and deploy it to the Google App Engine:
Then use a Flask framework to ingest data and invoke it using cron:3. Infrastructure AI is resource-intensive, in terms of both staff, and compute power. Many on-prem setups simply cannot handle the demands of AI. You can learn to take advantage of Google Cloud’s compute power to run your advanced AI jobs. The Google Cloud Dataflow to process data lab shows you how to configure BigQuery and install Python packages to use Apache Beam:
Then you will monitor the progress of your Cloud Dataflow job and inspect the processed data:
Need more practice with the GCP infrastructure? Visualize data with Google Data Studio by running the query to get the IP address to connect to the Cloud SQL:
Then create table views to look at flights that are delayed by 10, 15, 20 minutes:
After connecting with the Data Studio, you’ll create a data visualization for flight delays:
Tackle the quest to practice ingestion, preparation, processing, querying, exploring and visualizing of data sets using GCP. When you earn the badge, let us know. You can find us at @Qwiklabs – and we’re always happy to hear about your accomplishments!
Want to make your life easier by moving from on-prem to the cloud, but don’t know where to start? There’s a Quest for that. The Baseline: Infrastructure Quest gives you hands-on practice with GCP’s core infrastructure services like Cloud IAM, Kubernetes, and Stackdriver. Enroll in the Quest by Monday, September 24th you’ll get a 1-month pass (free of charge, no CC required) to earn the badge and show your “flight time” with Google Cloud.
Even better, each lab in the Quest has 1-minute videos to walk you through key concepts for each lab. Here are some of the labs in the Quest:
- Cloud Storage: Qwik Start – Console: Use the Google Cloud Platform Console to create a storage bucket, upload objects, create folders & subfolders, and then make those objects publicly accessible.
When you’ve created a bucket & uploaded an object into the bucket, you can check your progress:
Ensure that you’ve understood each concept by answering multiple choice questions:
How did you do? Share your results @Qwiklabs. Need extra help? Watch Jenny as she walks you through this lab.
- Cloud IAM: Qwik Start: Cloud IAM unifies access control for GCP services into a single system and presents a consistent set of operations. Learn to create and manage permissions with this lab. You’ll assign and remove roles associated with Identity and Access Management (IAM).
When you’ve removed project viewer access for a user with IAM, you should see a similar permission error:
If not, let Jenny come to your rescue in this video!
- Kubernetes Engine: Qwik Start: After you’ve created a cluster, you’ll execute kubectl run command to create a new Deployment, hello-server, using the hello-app container image. Before you can inspect the hello-server service, you’ll need to expose your application to external traffic:
If you’re unable to view the application from your web browser using the external IP address with the exposed port, let Jenny help you!
- Deployment Manager: Qwik Start: Since Deployment Manager is an infrastructure deployment service that automates the creation and management of GCP resources, you can write configuration files for Cloud Storage, Compute Engine, Cloud SQL, etc.
In this lab, you’ll write a file to create & deploy configuration, inspect the running environment and view the deployment manifest. While waiting to deploy your configuration, you’ll see a status message:
Don’t see it? Jenny can help you with this lab too!
Why wait? Enroll now before 2020 hits, and your server closet in the back room is still humming (:
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.
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).
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!