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

Chapter 3: Machine Learning Algorithms

Machine learning (ML) algorithm design is usually not the main focus for a practitioner of ML solutions architecture. However, ML solutions architects still need to develop a solid understanding of the common real-world ML algorithms and how those algorithms solve real business problems. Without this understanding, you will find it difficult to identify the right data science solutions for the problem at hand and design the appropriate technology infrastructure to run these algorithms.

In this chapter, you will develop a deeper understanding of how ML works first. We will then cover some common ML and deep learning algorithms for the different ML tasks, such as classification, regression, object detection, recommendation, forecasting, and natural language generation. You will learn the core concepts behind these algorithms, their advantages and disadvantages, and where to apply them in the real world. Specifically, we are going to cover the...