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)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Chapter 12: Building ML Solutions with AWS AI Services

You have come a long way and we are getting close to the finishing line. Up to this point, we have mainly focused on the skills and technologies required to build and deploy ML models using open source technologies and managed ML platforms. To solve business problems with machine learning, however, you don't always have to build, train, and deploy your ML models from scratch. An alternative option is to use fully managed AI services. AI services are fully managed APIs or applications with pre-trained models that perform specific ML tasks, such as object detection or sentiment analysis. Some AI services also allow you to train custom models with your data for a defined ML task, such as document classification. AI services promise to enable organizations to build ML-enabled solutions without requiring strong ML competencies.

In this final chapter, we are going to switch gears and talk about several AWS AI services and where...