Part 2 of 3: MLOps is a solution to the paradox of AI

In case you missed it: please find part 1 of this article series over here.

In that Part 1 of this article series, we highlighted the existing paradox in the AI industry where organizations recognize the massive potential benefits of AI systems like ChatGPT but face significant challenges due to perceived complexities. These complexities include

• ecosystem integration complexities involving multiple data sources, targets, cadences, and types;
• engineering complexities such as pre-processing, efficiency, and framework choices; and
• operational complexities related to availability, maintenance, and data governance.

To address this paradox, organizations must strike a balance between leveraging the benefits of AI while managing its complexities effectively, with successful companies likely to gain a competitive advantage. In this second part of the article series, I would like to propose a particular solution to this balancing act. This solution is called MLOps.

About MLOps

MLOps (Machine Learning Operations) is a set of practices and tools that help organizations manage the lifecycle of machine learning (ML) models. It encompasses the entire process of developing, deploying, and maintaining ML models, from data collection and preparation to model training, testing, deployment, and monitoring. The goal of MLOps is to ensure that ML models are reliable, scalable, and perform as expected in production environments. It also aims to streamline the ML development process, making it more efficient and reproducible.

MLOps is becoming increasingly important as organizations adopt ML systems at scale, and borrows many of its ideas and concepts from the world of software engineering, often referred to as DevOps (Developer Operations). By implementing MLOps practices, organizations can improve the quality and reliability of their ML models, reduce the risk of model failures, and accelerate the time it takes to bring ML models to production. MLOps can help us productionize AI systems more efficiently, by

• automating the different steps in the process

• Aligning the different teams that are responsible for these different steps

Automating the processes and aligning the teams around these processes is what is going to allow organizations to seize the value that is being promised by AI, while at the same time managing and potentially even reducing the complexity of these systems. MLOps is the antidote to the potential poisoning of AI technology with unwieldy complexity.

So to summarize: the thesis of this second part of this article series, is that there is a solution possible to the paradox that we are currently seeing in the AI industry, and that this solution presents itself as a series of technological tools, processes and team alignment best practices. The question therefore becomes how we can easily and efficiently implement the practices of MLOps. This is likely not to be a trivial task, as we will be touching tools, processes and people during the implementation of MLOps in our organization. This, therefore, is what we will be discussing in the third and final part of this article series.