Feature Sharing and Discovery Using the Databricks Feature Store
This course will teach you how you can store, access, manage, and share your preprocessed machine learning features using the Databricks Feature Store.
What you'll learn
Converting raw data to features is an extremely important part of the machine learning workflow. Machine learning models are not trained on raw data, instead, they require preprocessed features that help built robust models.
In this course, Feature Sharing and Discovery Using the Databricks Feature Store, you will learn to create and use precomputed features from a centralized repository, the feature store, and the importance of feature stores and how they can help improve the machine-learning process workflow.
First, you will create and populate features in offline stores using the feature store client API, and overwrite existing features and merge new features into a store.
Next, you will learn how you can use feature lookup objects to create training sets to train machine learning models using features stored in feature tables.
Then, you will join feature store records with rows in a data frame to create training data, and log models using the feature store client and use this model to perform batch inference on your data.
Finally, you will see how you can publish your batch features to an online feature store that uses a low-latency database such as Azure Cosmos DB to store features for real-time serving. You will deploy a model to a REST endpoint and use features from the online store for real-time serving.
When you are finished with this course, you will have the skills and knowledge to use the Databricks feature store to precompute, store, and access features to train machine learning models.
Table of contents
- Prerequisites and Course Outline 2m
- The Databricks Feature Store 5m
- Feature Store Concepts 4m
- Demo: Setting up the Databricks Environment 5m
- Demo: Loading and Preprocessing Raw Data 3m
- Demo: Creating and Populating Feature Tables 8m
- Demo: Accessing Feature Table Metadata and Contents 1m
- Demo: Feature Tables Are Delta Tables under the Hood 4m
- Demo: Overwriting and Merging Records in a Feature Table 3m
- Demo: Creating a Feature Table with Partition Columns 5m
- Demo: Adding New Features to an Existing Feature Table - I 4m
- Demo: Adding New Features to an Existing Feature Table - II 4m
- Demo: Adding, Updating, and Deleting Tags 2m
- Demo: Populating Feature Tables Using Streaming Data 8m
- Demo: Deleting Feature Tables 4m
- Training Datasets and Model Inference 2m
- Demo: Preprocessing Raw Features and Creating a Feature Table 4m
- Demo: Using Features from a Feature Table to Train Models 6m
- Demo: Exploring Tracking Metrics and Parameters 3m
- Demo: Using Features from a DataFrame and Feature Table to Train Models 4m
- Demo: Making Model Predictions Using Features from the Feature Store 2m
- Demo: Using Features from Multiple Feature Tables to Train Models 6m
- Online Feature Stores 5m
- Demo: Creating an Offline Feature Store 3m
- Demo: Creating an Azure Cosmos DB Account Database and Container 5m
- Demo: Configuring Secrets to Access Cosmos DB from Databricks 5m
- Demo: Publish Features to the Cosmos DB Online Store 5m
- Demo: Using Features from an Online Store for Real-time Inferencing 7m
- Summary and Further Study 1m