Book Image

Machine Learning on Kubernetes

By : Faisal Masood, Ross Brigoli
Book Image

Machine Learning on Kubernetes

By: Faisal Masood, Ross Brigoli

Overview of this book

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.
Table of Contents (16 chapters)
1
Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
5
Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes
10
Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform

Performing exploratory data analysis

At this stage, you analyze the data to assess its suitability for the given problem. Data analysis is essential for building ML models. Before you create an ML model, you need to understand the context of the data. Analyzing vast amounts of company data and converting it into a useful result is extremely difficult, and there is no single answer on how to do it. Figuring out what data is meaningful and what data is vital for business is the foundation for your ML model.

This is a preliminary analysis, and it does not guarantee that the model will bring the expected results. However, it provides an opportunity to understand the data at a higher level and pivot if required.

Understanding sample data

When you get a set of data, you first try to understand it by merely looking at it. You then go through the business problem and try to determine what set of patterns would be helpful for the given situation. A lot of the time, you will need to...