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Engineering MLOps

You're reading from  Engineering MLOps

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Emmanuel Raj Emmanuel Raj
Profile icon Emmanuel Raj

Table of Contents (18) Chapters

Preface Section 1: Framework for Building Machine Learning Models
Chapter 1: Fundamentals of an MLOps Workflow Chapter 2: Characterizing Your Machine Learning Problem Chapter 3: Code Meets Data Chapter 4: Machine Learning Pipelines Chapter 5: Model Evaluation and Packaging Section 2: Deploying Machine Learning Models at Scale
Chapter 6: Key Principles for Deploying Your ML System Chapter 7: Building Robust CI/CD Pipelines Chapter 8: APIs and Microservice Management Chapter 9: Testing and Securing Your ML Solution Chapter 10: Essentials of Production Release Section 3: Monitoring Machine Learning Models in Production
Chapter 11: Key Principles for Monitoring Your ML System Chapter 12: Model Serving and Monitoring Chapter 13: Governing the ML System for Continual Learning Other Books You May Enjoy

Business problem analysis and categorizing the problem

In the previous chapter, we looked into the following business problem statement. In this section, we will demystify the problem statement by categorizing it using the principles to curate an implementation roadmap. We will glance at the dataset given to us to address the business problem and decide what type of ML model will address the business problem efficiently. Lastly, we'll categorize the MLOps approach for implementing robust and scalable ML operations and decide on tools for implementation.

Here is the problem statement:

You work as a data scientist with a small team of data scientists for a cargo shipping company based in Finland. 90% of goods are imported into Finland via cargo shipping. You are tasked with saving 20% of the costs for cargo operations at the port of Turku, Finland. This can be achieved by developing an ML solution that predicts weather conditions at the port 4 hours in advance. You need to...

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