Navigating the ML Lifecycle with ML Solutions Architecture
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. More recently, Generative AI (GenAI) technology such as ChatGPT has been taking the industry by storm, generating huge interest among company executives and consumers alike, promising new ways to transform businesses such as drug discovery, new media content, financial report analysis, and consumer product design. 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 MLOps engineering, AI/ML product management, ML software engineering, AI risk manager, and AI strategist 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 lifecycle as ML projects become increasingly complex in terms of business impact, science sophistication, and the technology landscape.
This chapter will help you understand where ML solutions architecture fits in the full data science lifecycle. We will discuss the different steps it will take to get an ML project from the ideation stage to production and the challenges faced by organizations, such as use case identification, data quality issues, and shortage of ML talent 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.
In this chapter, we are going to cover the following main topics:
- ML versus traditional software
- The ML lifecycle and its key challenges
- What is ML solutions architecture, and where does it fit in the overall lifecycle?
Upon completing this chapter, 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. The intent of this chapter is to offer a fundamental introduction to the ML lifecycle for those in the early stages of their exploration in the field. Experienced ML practitioners may wish to skip this foundational overview and proceed directly to more advanced content.
The more advanced section commences in Chapter 4; however, many technical practitioners may find Chapter 2 helpful, as numerous technical practitioners often need more business understanding of where ML can be applied in different businesses and workflows. Additionally, Chapter 3, could prove beneficial for certain practitioners, as it provides an introduction to ML algorithms for those new to this topic and can also serve as a refresher for those practicing these concepts regularly.
