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

Overview of ML algorithms

A large number of ML algorithms have been developed to date, and more are being researched and invented at an accelerated pace by academia and industry alike. This section will review some popular traditional and deep learning algorithms and how these algorithms can be applied to different kinds of ML problems. But first, let's quickly discuss the considerations when choosing an ML algorithm for the task.

Consideration for choosing ML algorithms

There are a number of considerations when it comes to choosing ML algorithms for different tasks:

  • Training data size: Some ML algorithms, such as deep learning algorithms, can work very well and produce highly accurate models, but they require large amounts of the training data. Traditional ML algorithms, such as linear models, can work effectively when the dataset is small but cannot take advantage of large datasets as effectively as deep learning neural network algorithms. Traditional ML algorithms...