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Practical Machine Learning on Databricks

You're reading from  Practical Machine Learning on Databricks

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781801812030
Pages 244 pages
Edition 1st Edition
Languages
Author (1):
Debu Sinha Debu Sinha
Profile icon Debu Sinha

Table of Contents (16) Chapters

Preface 1. Part 1: Introduction
2. Chapter 1: The ML Process and Its Challenges 3. Chapter 2: Overview of ML on Databricks 4. Part 2: ML Pipeline Components and Implementation
5. Chapter 3: Utilizing the Feature Store 6. Chapter 4: Understanding MLflow Components on Databricks 7. Chapter 5: Create a Baseline Model Using Databricks AutoML 8. Part 3: ML Governance and Deployment
9. Chapter 6: Model Versioning and Webhooks 10. Chapter 7: Model Deployment Approaches 11. Chapter 8: Automating ML Workflows Using Databricks Jobs 12. Chapter 9: Model Drift Detection and Retraining 13. Chapter 10: Using CI/CD to Automate Model Retraining and Redeployment 14. Index 15. Other Books You May Enjoy

Understanding the need for AutoML

If you have never worked with any AutoML framework before, you might be wondering what AutoML is and when and how it can be useful.

AutoML simplifies the machine learning model development process by automating various tasks. It automatically generates baseline models tailored to your specific datasets and even offers preconfigured notebooks to kickstart your projects. This is particularly appealing to data scientists of all levels of expertise because it saves valuable time in the initial stages of model development. Instead of manually crafting models from scratch, AutoML provides a quick and efficient way to obtain baseline models, making it a valuable tool for both beginners and experienced data scientists alike.

AutoML makes machine learning not only accessible to citizen data scientists and business subject matter experts. AutoML, while undoubtedly a powerful tool, also grapples with significant limitations. One notable challenge is its...

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