ML Models on GC Training Overview
This live, online Creating and Deploying Machine Learning Models on Google Cloud (Scikit-Learn and TensorFlow Models) training course teaches attendees how to create machine learning models using the Scikit-Learn machine learning library. Students also learn how to leverage TensorFlow to build deep learning models. Participants then use GC’s artificial intelligence (AI) Platform to deploy their custom model to query their models for predictions.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
All students will learn how to:
- Deploy models on Google Cloud Platform (GCP)
- Understand how machines learn
- Understand structured and unstructured data
- Compare AI, machine learning, and deep learning
- Use common machine learning algorithms
- Use Scikit-Learn to create and train machine learning models
- Use TensorFlow and Keras to create and train deep learning models
- Use AI platform on GCP
- Use cloud storage on GCP
- Use AI platform Notebooks on GCP to build and train Scikit-Learn and TensorFlow machine learning models
- Use GCP to deploy trained machine learning models
- Understand the fundamental techniques through demos and hands-on labs
Prerequisites
All students must have Python experience and a basic understanding of Machine Learning.
Outline
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Introduction
Compare AI, ML, and DL
Understanding How Machines Learn
Structured vs. Unstructured Data
Installing Anaconda and TensorFlow
Common Machine Learning Algorithms
Using Scikit-Learn to create and train machine learning models
Using Scikit-Learn to Build a Linear and a Logistic Regression Model
Saving a Scikit-Learn Model
- pickle (model.pkl)
- joblib (model.joblib)
Using GCP Cloud Storage to Store Saved Models
Creating a Cloud Storage Bucket on GCP and Uploading Models
Introducing Keras/TensorFlow
Using Keras to Build a Linear Regression and a Neural Network Model
Saving a TensorFlow Model
Saving the Model in TensorFlow Format
Storing model in GCP Cloud Storage
Creating Different Model Versions for Deployment
Introducing AI Platform
AI Platform Notebooks
AI Platform Models
Deploy Models on GCP
Creating an AI Platform Model Resource and Version Resource Serve Models from GCP
Creating Input Data and Query Deployed Model for Predictions Next Steps
Structured Activity/Exercises/Case Studies
- Installing Anaconda and TensorFlow
- Using scikit-learn to build a linear and a logistic regression model
- Creating a Cloud Storage bucket on GCP and uploading models
- Using Keras to build a linear regression and a neural network model
- Creating an AI Platform model resource and version resource
- Creating input data and query deployed model for predictions
Conclusion
Training Materials
All Machine Learning Models on Google Cloud Platform training students receive comprehensive courseware.
Software Requirements
Students must have the Google Chrome web browser and Internet access.
