Building Machine Learning Models on Databricks

سرفصل های دوره

This course will teach you how you can build and train your traditional machine learning models using the Databricks Machine Learning runtime and MLflow to manage the end-to-end machine learning lifecycle.

1. Course Overview

1. Course Overview

2. Introducing Machine Learning on Databricks

1. Prequisites and Course Outline

2. Overview of Databricks

3. The Databricks Machine Learning Runtime

4. Introducing MLflow

5. Demo - Getting Set up with the Machine Learning Environment on Databricks

03. Implementing scikit-learn Models in Databricks

01. A Quick Overview of scikit-learn

02. Demo - Loading, Exploring, and Preprocessing Data

03. Demo - Creating an Experiment and Run

04. Demo - Autologging to Track Model Metrics

05. Demo - Creating Multiple Runs and Comparing Runs

06. Demo - Using Loaded Model for Predictions

07. Demo - Using Bamboolib for Data Exploration and Transformation

08. Demo - Autologging to Track Metrics for a Classification Model

09. Demo - Registering Models and Managing Stage Transitions

10. Demo - Classic Inferencing Using a REST Endpoint

4. Implementing XGBoost Models in Databricks

1. An Overview of XGBoost

2. Demo - Inferring Model Signature and Logging Models

3. Demo - Autologging XGBoost Model Runs

4. Machine Learning Using Apache Spark

5. Demo - Loading Data into a Delta Table

6. Demo - Training a Model Using a Spark ML Pipeline

7. Demo - Training an XGBoost Model Using a Spark Pipeline

8. Demo - Using Cross Validation to Find the Best Model Hyperparameters

5. Hyperparameter Tuning for Machine Learning Models

1. Understanding Hyperparameter Tuning

2. Hyperopt for Hyperparameter Tuning

3. Demo - Explicitly Logging Model Parameters

4. Demo - Hyperparameter Tuning Using Hyperopt

5. Demo - Hyperparameter Training Using Different Classifiers

6. Summary and Further Study

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تولید کننده: PluralSight