Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow

Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach

Key Features:

• Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow
• Use MLflow to iteratively develop a ML model and manage it
• Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environment

Book Description:

MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.

This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, youll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.

By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.

What You Will Learn:

• Develop your machine learning project locally with MLflows different features
• Set up a centralized MLflow tracking server to manage multiple MLflow experiments
• Create a model life cycle with MLflow by creating custom models
• Use feature streams to log model results with MLflow
• Develop the complete training pipeline infrastructure using MLflow features
• Set up an inference-based API pipeline and batch pipeline in MLflow
• Scale large volumes of data by integrating MLflow with high-performance big data libraries

Who this book is for:

This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.