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Modern Data Architectures with Python

You're reading from  Modern Data Architectures with Python

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Brian Lipp Brian Lipp
Profile icon Brian Lipp

Table of Contents (19) Chapters

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

AutoML

AutoML is the process of giving an AutoML program your data and having it try to find the best algorithm and hyperparameter for you. This can often give you great general results but typically, it requires more fine-tuning if your AutoML is done well. AutoML can be expensive, so it’s important to watch your bill and plan accordingly. Databricks offers a very useful AutoML feature. Databricks AutoML performs data preparation, trains several models, and then finds the best-trained models. Databricks AutoML will use a variety of the most popular ML libraries in the evaluation. So, will AutoML give you the best model possible? No, it’s not going to replace the need for further feature engineering and model tuning. Instead, it’s going to take a chunk of the work off your plate and try to give you a reasonable model to start with. In some cases, that model will be good enough for what you need.

Note

You can learn more about AutoML by going to https://learn...

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