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

Understanding the TensorFlow deep learning library

Initially released in 2015, TensorFlow is a popular open source machine learning library, primarily backed up by Google, that is mainly designed for deep learning. TensorFlow has been used by companies of all sizes for training and building state-of-the-art deep learning models for a range of use cases, including computer vision, speech recognition, question-answering, text summarization, forecasting, and robotics.

TensorFlow is based on the concept of a computational graph (that is, a dataflow graph), in which the data flow and operations that are performed on the data are constructed as a graph. TensorFlow takes input data in the form of an n-dimensional array/matrix, which is known as a tensor, and performs mathematical operations on this tensor, such as add or matrix multiplication. An example of a tensor could be a scalar value (for example, 1.0), a one-dimensional vector (for example, [1.0, 2.0, 3.0]), a two-dimensional...