Part 3: An AI Lakehouse can be a forcing function for MLOps

In the first and second part of this article series, we explored the paradox that organizations face when considering the adoption of AI systems. On the one hand, AI systems offer significant potential benefits, such as real-time operational input and improved decision-making. On the other hand, organizations are confronted with significant challenges, primarily related to the perceived complexity of AI systems.

Part 1 of the series identified three main categories of complexity: ecosystem integration complexity, engineering complexity, and operational complexity. Ecosystem integration complexity arises from the need to integrate AI systems with various input and output systems, which can involve multiple data sources, targets, cadences, and types. Engineering complexity stems from the extensive pre-processing, efficiency requirements, and diverse frameworks and languages used in AI systems. Operational complexity involves ensuring the availability and uptime of AI systems, maintaining and evolving them, and implementing and ensuring data governance.

Part 2 of the series introduced MLOps as a solution to the paradox of AI complexity. MLOps is a set of practices and tools that help organizations manage the lifecycle of machine learning (ML) models. 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.

In Part 3, we will delve deeper into the benefits of MLOps and explore how it can help organizations overcome the challenges of AI complexity. MLOps has implications on people and processes that are used to build AI systems, and in this third article we will discuss how we can maximize the chances of such a system to be successfully implemented. We will consider the key components of an MLOps platform, articulate how to use these components as forcing functions and provide practical tips and best practices for implementing MLOps in organizations.

At Hopsworks , we have worked with many clients that have successfully used and implemented an MLOps system - and they have done so using the Hopsworks AI Lakehouse.

The Hopsworks AI Lakehouse acts as a centralized repository around which all the different process steps in a machine learning system can be built. This will then help people, and force them to some degree, to adhere to the principles of MLOps by leveraging the AI Lakehouse:

• It brings together and integrates all the moving parts of an ML system, so that the individual engineer does not need to duct tape a system together with him- or herself.
• It integrates online and offline machine learning data into one coherent metadata-based system.
• It provides easy-api based access to the right data that is needed for the ML processes to take place. For example, it ensures point-in-time-correct joins that prevent leakage of future data into the predictive model that we want to use.
• It provides automated deployment procedures that allow devops best practices to be adopted by machine learning experts
• It guarantees reproducibility and auditability that is (or will be) requirement by internal or external AI rules and regulations

The core idea of the AI Lakehouse, therefore, becomes that of using centralized data as the forcing function for implementing all the required tools, processes and team interactions that we need to make AI a much more predictable, productive, and efficient endeavour.

Just to make sure we all understand: a forcing function is a factor or event that compels or influences a change or action. It is often used in project management, strategy development, and organizational change to create a sense of urgency and drive progress. The goal is to encourage individuals or teams to take action by creating a situation where they feel compelled to do so. Forcing functions can be internal or external. Internal forcing functions are typically related to the organization's goals, objectives, or values. External forcing functions are often driven by market conditions, technological advancements, or regulatory changes. Forcing functions can be powerful tools for driving change and innovation. By creating a sense of urgency and compelling action, they can help organizations overcome inertia and achieve their goals more quickly.

And that is exactly what the AI Lakehouse can provide in the context of the AI Paradox and MLOps as a solution strategy to that paradox: all different stakeholders will be organizing their processes around the AI Lakehouse, offering a central point of control for MLOps best practices to be implemented. This makes MLOps a reality, and allows us to use the power of these principles to resolve the AI paradox that we addressed in the first part of these three articles. Effectively, AI Lakehouses make AI systems easier to implement, faster to deploy, more economically viable and structurally compliant with your organizational context’s regulations. That is a pretty great thing!

Thank you for taking the time to read these three articles - I hope they will allow you to shape your thinking around AI projects.