The Machine Learning Solutions Architect Handbook

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key Features

• Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
• Build an efficient data science environment for data exploration, model building, and model training
• Learn how to implement ML bias, fairness, and explainability in the end-to-end ML lifecycle

Book Description

A highly scalable machine learning platform enables organizations to quickly scale the delivery of ML products for faster business value realization. There is also a huge demand for skillful ML solutions architects in different industries.

This handbook takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect. You'll start by understanding core machine learning fundamentals, and how ML can be applied to real-world business problems. Next, you'll explore some of the leading machine learning and deep learning algorithms for different types of ML problems. The book will further cover data management and architecture considerations for building data science environments using ML libraries such as scikit-learn, Spark, TensorFlow, and PyTorch. You'll then implement Kubernetes containers for orchestration infrastructure management and later build a data science environment and enterprise ML architecture using AWS ML services. Toward the end, you'll go through security and compliance considerations, advanced ML engineering techniques, and how to apply ML bias, fairness, and explainability in the end-to-end ML cycle.

By the end of this book, you'll be able to design and build an ML platform to support ML use cases and architecture patterns.

What you will learn

• Apply machine learning methodologies to solve business problems
• Design a practical enterprise machine learning platform architecture
• Implement MLOps for machine learning workflow automation
• Build an end-to-end data management architecture using Amazon Web Services (AWS)
• Create a business application using an AI service and custom ML model
• Use AWS to detect data and model bias

Who This Book Is For

This book is for data scientists, data analysts, and machine learning enthusiasts who want to become machine learning solutions architect professionals. Basic knowledge of the Python programming language is assumed.

Table of Contents

1. Machine learning and Machine Learning Solution Architecture
2. Business Use Cases for ML
3. Machine Learning Algorithms
4. Machine Learnings Tools and AWS Infrastructure for ML
5. Data Management and Engineering
6. Kubernetes Containers Orchestration Infrastructure Management
7. Open-Source Machine Learning Platforms
8. Building a Data Science Environment Using AWS ML Services
9. Building an Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. ML Bias, Fairness, Explainability, and Regulation
12. Designing ML Solutions Using AI Services and ML Platform