All things ML monitoring, from introductory topics on data and concept drift to architecture deep dives.
A beginner-friendly MLOps tutorial on how to evaluate ML data quality, data drift, model performance in production, and track them all over time using open-source tools.
Monitoring embedding drift is relevant for the production use of LLM and NLP models. We ran experiments to compare 5 drift detection methods. Here is what we found.
Imagine you have a machine learning model in production, and some features are very volatile. Their distributions are not stable. What should you do with those? Should you just throw them away?
There is an overwhelming set of potential metrics to monitor. In this blog, we'll try to introduce a reasonable hierarchy.
When one mentions "ML monitoring," this can mean many things. Are you tracking service latency? Model accuracy? Data quality? This blog organizes everything one can look at in a single framework.
We ran an experiment to help build an intuition on how popular drift detection methods behave. In this blog, we share the key takeaways and the code to run the tests on your data.
Data and prediction drift often need contextual interpretation. In this blog, we walk you through possible scenarios for when you detect these types of drift together or independently.
Even if you can calculate the model quality metric, monitoring data and prediction drift can be often useful. Let’s consider a few examples when it makes sense to track the distributions of the model inputs and outputs.
When monitoring ML models in production, we can apply different techniques. Data drift and outlier detection are among those. What is the difference? Here is a visual explanation.
Can you train a machine learning model to predict your model’s mistakes? Nothing stops you from trying. But chances are, you are better off without it.
No model lasts forever. While the data quality can be fine, the model itself can start degrading. A few terms are used in this context. Let’s dive in.
A bunch of things can go wrong with the data that goes into a machine learning model. Our goal is to catch them on time.
Garbage in is garbage out. Input data is a crucial component of a machine learning system. Whether or not you have immediate feedback, your monitoring starts here.
Who should care about machine learning monitoring? The short answer: everyone who cares about the model's impact on business.