Training: Hands-on With Google Cloud AutoML
This page will host content, resources and links relevant to running a 3-hour workshop or training session on Google Cloud AutoML targeting developers.
OVERVIEW
The focus is on Building High-Quality Custom ML Models with Minimal Effort. No prior expertise with machine learning or Google Cloud is expected. The training series kicks off in Q4 2018 and is offered through Pearson education on the Safara Live Training platform. The instructor will be Nitya Narasimhan as instructor. Training dates, times and registration links will be provided below as known.
| Date | Time | Registration Link |
|---|---|---|
| Oct 17, 2018 | 12-3pm EST | Register Here |
| Nov 16, 2018 | 12-3pm EST | TBA |
| Dec 17, 2018 | 1-4pm EST | TBA |
INSTRUCTOR
Nitya Narasimhan is a PhD with 20+ years of software development & research experience in distributed systems, mobile & web computing. She manages the Google Developer Group New York City (GDG NYC) chapter, organizes the DevFest NYC conference and speaks regularly on emerging technologies, cross-platform application development, machine learning and community. She is also a Google Developer Expert in Flutter and a technology educator and consultant based in New York.
- Follow her @nitya on Twitter.
- Connect with her #in/nityan on LinkedIn
- Learn more at https://nityan.me
OBJECTIVES
Machine learning and artificial intelligence are rapidly permeating all aspects of the technology ecosystem across enterprise and consumer domains. Thanks to the power of cloud computing, we now have machines that are trained to see (computer vision), talk (speech), understand (natural language processing) and even translate across different languages.
App developers can take advantage of pre-trained models for these features from cloud platform vendors like Google, integrating them using REST APIs exposed by those cloud ML services.But what if you wanted to have a custom model that was tailored more precisely to the needs and context for your domain – but weren’t comfortable enough with the data sciences knowledge required to work with TensorFlow or other ML frameworks?
AutoML can help. In this training, we’ll learn what AutoML is, and how we can use Google’s Cloud AutoML products to train custom models for Natural Language (to classify documents), Translation (to interpret queries) and Vision (to label images) with minimal effort.
What You’ll Learn
- Understand ideas behind AutoML like transfer learning and learning2learn
- Learn about Google’s Cloud AutoML products (Vision, Natural Language, Translation)
- Walk through the usage of each product with an example application and dataset
- Do deep dives into key aspects like data preparation & model evaluation
- Learn how AutoML differs from Cloud ML, TensorFlow & Firebase ML Kit options
SCHEDULE
Duration: 3 hours, 5 segments, 5-min breaks
The timeframes are only estimates and may vary according to how the class is progressing
Segment 1 / Introduction to AutoML
Content: 25 mins
Break: 5 mins
- What is the vision behind AutoML
- High level overview of Learning2Learn, Transfer Learning
- Where does AutoML fit into Google’s Machine Learning ecosystem?
- What is Cloud AutoML?
Segment 2 / AutoML Vision
Content: 45 mins
Break: 5 mins
- Challenge: Labeling Images accurately
- Your Options: Vision API vs. AutoML Vision
- AutoML Vision Training (walkthrough)
- Recap: Data Preparation
- Recap: Model Evaluation
- Recap: Model Testing
Segment 3 / AutoML Natural Language
Content: 35 mins
Break: 5 mins
- Challenge: Detect Entities & Sentiment in Conversation
- Your Options: NL API vs. AutoML Natural Language
- AutoML Natural Language Training (walkthrough)
- Recap: Data Preparation
- Recap: Model Evaluation
- Recap: Model Testing
Segment 4 / AutoML Translation
Content: 25 mins
Break: 5 mins
- Challenge: Translate query language
- Pick your option: Translation API vs. AutoML Translate
- AutoML Translate Training (walkthrough)
- Recap: Data Preparation
- Recap: Model Evaluation
- Recap: Model Testing
Segment 5 Putting It All Together
Content: 25 mins
Wrapup: 5 mins
- The Google ML Ecosystem: Cloud ML, Auto ML, TensorFlow, Firebase ML Kit
- What We Saw: Auto ML Vision, NL, Translation
- Where is it useful: Application Examples
- What are the challenges: Preparation & Evaluation
- Where next: Resources & Recommendations
RESOURCES
I’ll update this section on a dynamic basis, with references to relevant articles, codelabs, documentation and code examples, to help attendees continue their journey of self-guided learning around AutoML.