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

ML challenges

Over the years, I have worked on many real-world problems using ML solutions and encountered different challenges faced by the different industries during ML adoptions.

I often get this question when working on ML projects: We have a lot of data – can you help us figure out what insights we can generate using ML? This is called the business use case challenge. Not being able to identify business use cases for ML is a very big hurdle for many companies. Without a properly identified business problem and its value proposition and benefit, it would be challenging to get an ML project off the ground.

When I have conversations with different companies across their industries, I normally ask them what the top challenge for ML is. One of the most frequent answers I always get is about data – that is, data quality, data inventory, data accessibility, data governance, and data availability. This problem affects both data-poor and data-rich companies and is often exacerbated by data silos, data security, and industry regulations.

The shortage of data science and ML talent is another major challenge I have heard from many companies. Companies, in general, are having a tough time attracting and retaining top ML talent, which is a common problem across all industries. As the ML platform becomes more complex and the scope of ML projects increases, the need for other ML-related functions starts to surface. Nowadays, in addition to just data scientists, an organization would also need function roles for ML product management, ML infrastructure engineering, and ML operations management.

Through my experiences, another key challenge that many companies have shared is gaining cultural acceptance of ML-based solutions. Many people treat ML as a threat to their job functions. Their lack of knowledge of ML makes them uncomfortable in adopting these new methods in their business workflow.

The practice of ML solutions architecture aims to help solve some of the challenges in ML. Next, let's take a closer look at ML solutions architecture and its place in the ML life cycle.