Operationalizing Machine Learning Pipelines

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, youll 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 models 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 models performance, youll be able to train and deploy it more quickly and with greater confidence.

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.

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.

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

Vishwajyoti is a data professional with 15 years of industry experience in data related solutions including BI, Datawarehouse, Machine Learning and Data Science. He has worked across multiple industries like Retail, Banking, Energy, IoT and Manufacturing for customers across various geographies. He has implemented enterprise level ML practice across many organizations and solved a variety of business use cases, providing positive impact to their business. Vishwajyoti also provides training and mentors professionals interested in learning and enhancing their data science skills.

He has experience in languages like Python, R, SQL and multiple databases. He has also implemented solutions on different cloud vendors. He has done B. Tech from NIT Nagpur (VNIT) and MSc in Marketing Analytics from Ghent University.

LinkedIn Profile: Vishwajyoti

Shaleen is a data scientist with 4 years of professional and research experience in industry verticals such as retail, travel & hospitality, and healthcare. He has also published three research papers about music auto-tagging, and medical image segmentation in well-known journals.

Shaleen has experience working with Python, C++, Javascript, and Swift as well as multiple SQL and NoSQL databases. He graduated from BITS Pilani, Dubai Campus with a B.E. in Computer Science.

LinkedIn Profile: Shaleen