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 10: Advanced ML Engineering

Congratulations on making it so far. By now, you should have developed a good understanding of the core fundamental skills that a machine learning (ML) solutions architect needs to work effectively across different phases of the ML life cycle. In this chapter, we will dive deep into several advanced ML topics. Specifically, we will cover various distributed model training options for large models and large datasets. We will also discuss the various technical approaches for reducing model inference latency. We will close this chapter with a hands-on lab on distributed model training.

Specifically, we will cover the following topics in this chapter:

  • Training large-scale models with distributed training
  • Achieving low latency model inference
  • Hands-on lab – running distributed model training with PyTorch