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Machine Learning Engineering with MLflow

You're reading from   Machine Learning Engineering with MLflow Manage the end-to-end machine learning life cycle with MLflow

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Length 248 pages
Edition 1st Edition
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Author (1):
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Natu Lauchande Natu Lauchande
Author Profile Icon Natu Lauchande
Natu Lauchande
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Problem Framing and Introductions
2. Chapter 1: Introducing MLflow FREE CHAPTER 3. Chapter 2: Your Machine Learning Project 4. Section 2: Model Development and Experimentation
5. Chapter 3: Your Data Science Workbench 6. Chapter 4: Experiment Management in MLflow 7. Chapter 5: Managing Models with MLflow 8. Section 3: Machine Learning in Production
9. Chapter 6: Introducing ML Systems Architecture 10. Chapter 7: Data and Feature Management 11. Chapter 8: Training Models with MLflow 12. Chapter 9: Deployment and Inference with MLflow 13. Section 4: Advanced Topics
14. Chapter 10: Scaling Up Your Machine Learning Workflow 15. Chapter 11: Performance Monitoring 16. Chapter 12: Advanced Topics with MLflow 17. Other Books You May Enjoy

Chapter 7: Data and Feature Management

In this chapter, we will add a feature management data layer to the machine learning platform being built. We will leverage the features of the MLflow Projects module to structure our data pipeline.

Specifically, we will look at the following sections in this chapter:

  • Structuring your data pipeline project
  • Acquiring stock data
  • Checking data quality
  • Managing features

In this chapter, we will acquire relevant data to provide datasets for training. Our primary resource will be the Yahoo Finance Data for BTC dataset. Alongside that data, we will acquire the following extra datasets.

Leveraging our productionization architecture introduced in Chapter 6, Introducing ML Systems Architecture, represented in Figure 7.1, the feature and data component is responsible for acquiring data from sources and making the data available in a format consumable by the different components of the platform:

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