Artificial intelligence (AI) is an extremely valuable technology, but it comes with risks that every business faces when using models in production. Recently, O’Reilly partnered with our team at Fiddler to bring you a free eBook on building high performance Responsible AI. This book contains the key strategies and frameworks we’ve learned for building AI safely and responsibly, so you can prevent issues like model bias and data drift from negatively affecting your customers and your business.
Over the next few weeks, we’ll be going chapter by chapter to share some of the most valuable insights from this book. Let’s start with Chapter 1: Introduction to Model Performance Management and Explainability.
Machine learning (ML) models are highly complex, data-driven systems that present new challenges to software teams. Model Performance Management tracks and monitors the performance of ML models through all stages of the model lifecycle. This lifecycle includes:
Machine learning models are quite different from traditional software systems, causing teams to struggle with maintaining high-quality models in production.
Model performance management has several key benefits that help teams address these challenges.
Before deploying a model, it’s important to validate that it solves the intended business problem and doesn’t have any adverse effects. MPM can help you explain the model in human-understandable terms, so you can answer questions like “How is the model making a prediction?” and “Are there any biases?”
Machine learning models are trained on historical data, and when the live data shifts (such as the COVID-19 pandemic causing shifts in consumer behavior), the model’s performance can degrade. MPM can reassess the model’s business value and performance on an ongoing basis through model monitoring.
It’s impossible to eliminate bias from the world. But we can work to eliminate bias from ML models, to make sure they’re not amplifying or propagating the bias reflected in the real-world data (like the famous case of Amazon’s ML models for recruiting favoring resumes with traditionally male first names). MPM monitors for bias and helps businesses address it immediately to avoid costly penalties, such as regulatory fines or reputational loss.
Because MPM tracks a model’s behavior from training to serving, it can explain what factors led to a certain prediction to be made at a given time in the past. Explainable AI is vital for validation and compliance, allowing stakeholders to reproduce a model’s predictions along with explanations (this is known as “prediction time travel”).
As you may have noticed, one of the core features of MPM is being able to explain a model. Explainability is the subject of Chapter 2 of the O’Reilly eBook — stay tuned for a post on our blog that covers the key takeaways from that next chapter. You can download the full eBook for free by clicking here: