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Modern Data Architectures with Python

You're reading from   Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Length 318 pages
Edition 1st Edition
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Author (1):
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Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER 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

MLOps

ML and AI is one of the most important topics in the past several years. MLOps is the practice of productionizing ML products that have been created from data science research. MLOps is very important not only for reusability but also for clear and accurate data science. In this chapter, we will go through the ins and outs of MLflow, a popular MLOps tool that manages every stage of your data science project and experiments. We will also cover AutoML, which is an automated way to get reasonable ML models and feature stores. These are data management systems that version data for historical purposes.

In this chapter, we’re going to cover the following main topics:

  • The basics of machine learning
  • MLFlow
  • HyperOpt
  • AutoML
  • FeatureStore
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