Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Table of Contents (12 chapters)
10
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11
Index

Designing our forecasting service

The requirements in the Understanding the forecasting problem section are the definitions of the targets we need to hit, but they are not the method for getting there. Drawing on our understanding of design and architecture from Chapter 5, Deployment Patterns and Tools, we can start building out our design.

First, we should confirm what kind of design we should be working on. Since we need dynamic requests, it makes sense that we follow the microservice architecture discussed in Chapter 5, Deployment Patterns and Tools. This will allow us to build a service that has the sole focus of retrieving the right model from our model store and performing the requested inference. The prediction service should therefore have interfaces available between the dashboard and the model store.

Furthermore, since a user may want to work with a few different store combinations in any one session and maybe switch back and forth between the forecasts of these...