Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
1 (1)
Book Image

Machine Learning Engineering with Python - Second Edition

1 (1)
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)
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Option 2 is that training runs in batch, while prediction runs in whatever mode is deemed appropriate, with the prediction solution reading in the trained model from a store. We will call this design pattern train-persist. This is shown in the following diagram:

Figure 3.3 – The train-persist process

If we are going to train our model and then persist the model so that it can be picked up later by a prediction process, then we need to ensure a few things are in place:

  • What are our model storage options?
  • Is there a clear mechanism for accessing our model store (writing to and reading from)?
  • How often should we train versus how often will we predict?

In our case, we will solve the first two questions by using MLflow, which we introduced in Chapter 2, The Machine Learning Development Process, but will revisit in later sections. There are also lots of other solutions available. The key point is that no matter what you use as a model store and handover point between...