Testing Machine Learning Models

More companies are building ML and AI systems and applications, but they lack the same rigorous testing because many Testers don't know how to approach testing them. After having built and tested many different ML models and systems and talking to ML teams of small and large organizations, one thing always stands out: "We need better testing and automation in our MLOps lifecycle." So, we'll start by demystifying Machine Learning by breaking down a Prediction application so the audience better understands the "magic algorithms" behind the scenes. We'll explore an example of ML systems that didn't have proper testing (resulting in millions of dollars lost). They will learn what MLOps is and then we can dive into Behavioral Testing examples that are close to what we do as testers currently, but with an ML twist. We'll learn about Adversarial Attacks and how we can exploit security weaknesses and "fool" the model. Lastly, we'll discuss AI Fairness and how testing can help us catch and prevent harmful biases to protect our users. We need more Testers in the world of AI and this presentation is a great way to get them started on that path.