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

Summary

In this chapter, we covered topics surrounding AI services. We went over a list of AWS AI services and where they can be used to build ML solutions. We also talked about adopting MLOps for AI services deployment. Now, you should have a good understanding of what AI services are and know that you don't need to always build custom models to solve ML problems. AI services provide you with a quick way to build AI-enabled applications when they are a good fit.

Hopefully, this book has provided you with a good view of what the ML solutions architecture is and how to apply the various data science knowledge and ML skills to the different ML tasks at hand, such as building an ML platform. It is exciting to be an ML solutions architect now, as you have a broad view of the ML landscape to help different organizations drive digital and business transformation across different industries.

So, what's next? AI/ML is a broad field with many sub-domains, so developing technical...