Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. 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 like a true professional.
Table of Contents (17 chapters)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Designing an MLOps architecture for AI services

Implementing custom AI service models requires a data engineering, model training, and model deployment pipeline. This process is similar to the process of building, training, and deploying models using an ML platform. As such, we can also adopt MLOps practice for AI services when running them at scale.

Fundamentally, MLOps for AI services intends to deliver similar benefits as MLOps for the ML platform, including process consistency, tooling reusability, reproducibility, delivery scalability, and auditability. Architecturally, we can implement a similar MLOps pattern for AI services.

AWS account setup strategy for AI services and MLOps

To isolate the different environments, we can adopt a multi-account strategy for configuring the MLOps environment for AI services. The following diagram illustrates a design pattern for a multi-account AWS environment. Depending on your organizational requirement for separation of duty and control...