The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

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 bias detection, privacy, and explainability in ML model development

Book Description

With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book 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 ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.

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

What you will learn

• Apply ML methodologies to solve business problems
• Design a practical enterprise ML platform architecture
• Implement MLOps for ML workflow automation
• Build an end-to-end data management architecture using AWS
• Train large-scale ML models and optimize model inference latency
• Create a business application using an AI service and a custom ML model
• Use AWS services to detect data and model bias and explain models

Who this book is for

This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.

Table of Contents

1. Machine Learning and Machine Learning Solutions Architecture
2. Business Use Cases for Machine Learning
3. Machine Learning Algorithms
4. Data Management for Machine Learning
5. Open Source Machine Learning Libraries
6. Kubernetes Container 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 Governance, Bias, Explainability, and Privacy
12. Building ML Solutions with AWS AI Services

David Ping is a senior technology leader with over 25 years of experience in the technology and financial services industry. His technology focus areas include cloud architecture, enterprise ML platform design, large-scale model training, intelligent document processing, intelligent media processing, intelligent search, and data platforms. He currently leads an AI/ML solutions architecture team at AWS, where he helps global companies design and build AI/ML solutions in the AWS cloud. Before joining AWS, David held various senior technology leadership roles at Credit Suisse and JPMorgan. He started his career as a software engineer at Intel. David has an engineering degree from Cornell University.