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 discussed how a data science environment can provide a scalable infrastructure for experimentation, model training, and model deployment for testing purposes. You learned about the core architecture components for building a fully managed data science environment using AWS services such as Amazon SageMaker, Amazon ECR, AWS CodeCommit, and Amazon S3. You also practiced setting up a data science environment and trained and deployed an NLP model using both SageMaker Studio Notebook and SageMaker Training Service. At this point, you should be able to talk about the key components of a data science environment, as well as how to build one using AWS services and use it for model building, training, and deployment. In the next chapter, we will talk about how to build an enterprise ML platform for scale through automation.