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

Understanding the scikit-learn machine learning library

scikit-learn (https://scikit-learn.org/) is an open source machine learning library for Python. Initially released in 2007, it is one of the most popular machine learning libraries for solving many machine learning tasks, such as classification, regression, clustering, and dimensionality reduction.

scikit-learn is widely used by companies in different industries and academics for solving real-world business cases such as churn prediction, customer segmentation, recommendations, and fraud detection.

scikit-learn is built mainly on top of three foundational libraries: NumPy, SciPy, and matplotlib. NumPy is a Python-based library for managing large, multidimensional arrays and matrices, with additional mathematical functions to operate on the arrays and matrices. SciPy provides scientific computing functionality, such as optimization, linear algebra, and Fourier Transform. Matplotlib is used for plotting data for data visualization...