Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)

Deploy, manage, and scale Machine Learning models with MLOps effortlessly

Key Features
● Explore several ways to build and deploy ML models in production using an automated CI/CD pipeline.
● Develop and convert ML apps into Android and Windows apps.
● Learn how to implement ML model deployment on popular cloud platforms, including Azure, GCP, and AWS.

Description
‘Machine Learning in Production’ is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production.

It starts off with fundamental concepts, an introduction to the ML lifecycle and MLOps, followed by comprehensive step-by-step instructions on how to develop a package for ML code from scratch that can be installed using pip. It then covers MLflow for ML life cycle management, CI/CD pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides guidance on how to convert Python applications into Android and Windows apps, as well as how to develop ML web apps. Finally, it covers monitoring, the critical topic of machine learning attacks, and A/B testing.

With this book, you can easily build and deploy machine learning solutions in production.

What you will learn
● Master the Machine Learning lifecycle with MLOps.
● Learn best practices for managing ML models at scale.
● Streamline your ML workflow with MLFlow.
● Implement monitoring solutions using whylogs, WhyLabs, Grafana, and Prometheus.
● Use Docker and Kubernetes for ML deployment.

Who this book is for
Whether you are a Data scientist, ML engineer, DevOps professional, Software engineer, or Cloud architect, this book will help you get your machine learning models into production quickly and efficiently.

Table of Contents
1. Python 101
2. Git and GitHub Fundamentals
3. Challenges in ML Model Deployment
4. Packaging ML Models
5. MLflow-Platform to Manage the ML Life Cycle
6. Docker for ML
7. Build ML Web Apps Using API
8. Build Native ML Apps
9. CI/CD for ML
10. Deploying ML Models on Heroku
11. Deploying ML Models on Microsoft Azure
12. Deploying ML Models on Google Cloud Platform
13. Deploying ML Models on Amazon Web Services
14. Monitoring and Debugging
15. Post-Productionizing ML Models