David Ping - The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

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Early Access Publication: The Machine Learning Solutions Architect Handbook

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ISBN: 978-1-80107-216-8

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1. Machine learning and Machine Learning Solutions Architecture
2. Business Use Cases for Machine Learning
3. Machine Learning Algorithms
4. Data Management for Machine Learning
5. Open Source Machine Learning Libraries
6. Kubernetes Containers Orchestration Infrastructure Management
7. Open-Source Machine Learning Platforms
8. Building a Data Science Environment Using AWS ML Services
9. Building an Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. ML Bias, Fairness, Explainability, and Regulation
12. Designing ML Solutions Using AI Services and ML Platform

The field of artificial intelligence (AI) and machine learning (ML) has had a long history. Over the last 70+ years, ML has evolved from checker game-playing computer programs in the 1950s to advanced AI capable of beating the human world champion in the game of Go. Along the way, the technology infrastructure for ML has also evolved from a single machine/server for small experiments and models to highly complex end-to-end ML platforms capable of training, managing, and deploying tens of thousands of ML models. The hyper-growth in the AI/ML field has resulted in the creation of many new professional roles, such as MLOpsengineering, ML product management, and ML software engineering across a range of industries.

Machine learning solutions architecture (ML solutions architecture) is another relatively new discipline that is playing an increasingly critical role in the full end-to-end ML life cycle as ML projects become increasingly complex in terms of business impact, science sophistication, and the technology landscape.

This chapter talks about the basic concepts of ML and where ML solutions architecture fits in the full data science life cycle. You will learn the three main types of ML, including supervised, unsupervised, and reinforcement learning. We will discuss the different steps it will take to get an ML project from the ideas stage to production and the challenges faced by organizations when implementing an ML initiative. Finally, we will finish the chapter by briefly discussing the core focus areas of ML solutions architecture, including system architecture, workflow automation, and security and compliance.

Upon completing this chapter, you should be able to identify the three main ML types and what type of problems they are designed to solve. You will understand the role of an ML solutions architect and what business and technology areas you need to focus on to support end-to-end ML initiatives.

In this chapter, we are going to cover the following main topics:

• What is ML, and how does it work?
• The ML life cycle and its key challenges
• What is ML solutions architecture, and where does it fit in the overall life cycle?

AI can be defined as a machine demonstrating intelligence similar to that of human natural intelligence, such as distinguishing different types of flowers through vision, understanding languages, or driving cars. Having AI capability does not necessarily mean a system has to be powered only by ML. An AI system can also be powered by other techniques, such as rule-based engines. ML is a form of AI that learns how to perform a task using different learning techniques, such as learning from examples using historical data or learning by trial and error. An example of ML would be making credit decisions using an ML algorithm with access to historical credit decision data.

Deep learning (DL) is a subset of ML that uses a large number of artificial neurons (known as an artificial neural network) to learn, which is similar to how a human brain learns. An example of a deep learning-based solution is the AmazonEchovirtual assistant. To better understand how ML works, lets first talk about the different approaches taken by machines to learn. They are as follows:

• Supervised machine learning
• Unsupervised machine learning
• Reinforcement learning

Lets have a look at each one of them in detail.

Supervised ML is a type of ML where, when training an ML model, an ML algorithm is provided with the input data features (for example, the size and zip code of houses) and the answers, also known as