Implementing ML pipelines using MLOps

Key Features
In-depth knowledge of MLOps, including recommendations for tools and processes.
Includes only open-source cloud-agnostic tools for demonstrating MLOps.
Covers end-to-end examples of implementing the whole process on Google Cloud Platform.

Description
This book will provide you with an in-depth understanding of MLOps and how you can use it inside an enterprise. Each tool discussed in this book has been thoroughly examined, providing examples of how to install and use them, as well as sample data.

This book will teach you about every stage of the machine learning lifecycle and how to implement them within an organisation using a machine learning framework. With GitOps, you'll learn how to automate operations and create reusable components such as feature stores for use in various contexts. You will learn to create a server-less training and deployment platform that scales automatically based on demand. You will learn about Polyaxon for machine learning model training, and KFServing, for model deployment. Additionally, you will understand how you should monitor machine learning models in production and what factors can degrade the model's performance.

You can apply the knowledge gained from this book to adopt MLOps in your organisation and tailor the requirements to your specific project. As you keep an eye on the model's performance, you'll be able to train and deploy it more quickly and with greater confidence.

What you will learn
Quick grasp of the entire machine learning lifecycle and tricks to manage all components.
Learn to train and validate machine learning models for scalability.
Get to know the pros of cloud computing for scaling ML operations.
Covers aspects of ML operations, such as reproducibility and scalability, in detail.
Get to know how to monitor machine learning models in production.
Learn and practice automating the ML training and deployment processes.

Who this book is for
This book is intended for machine learning specialists, data scientists, and data engineers who wish to improve and increase their MLOps knowledge to streamline machine learning initiatives. Readers with a working knowledge of the machine learning lifecycle would be advantageous.

Table of Contents
1. DS/ML Projects Initial Setup
2. ML Projects Lifecycle
3. ML Architecture Framework and Components
4. Data Exploration and Quantifying Business Problem
5. Training & Testing ML model
6. ML model performance measurement
7. CRUD operations with different JavaScript frameworks
8. Feature Store
9. Building ML Pipeline

 

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Spis treści

Operationalizing Machine Learning Pipelines eBook -- spis treści