MLOps 101: A Fresh Approach to Managing Models with Event Streams
In data science, it’s important to be able to reproduce and understand our results. However, storing and managing our data can be challenging, especially when using relational databases. These databases are great for everyday operations, but can make it difficult to reproduce our work accurately.
When we deploy our models in real-world applications and APIs, the situation becomes even trickier. We need to keep our models up to date and ensure they make accurate predictions. Monitoring how models perform in production is crucial, but comparing models trained on different sets of data can be problematic, especially when dealing with high-dimensional data.
In this tutorial, we introduce a fresh approach to managing data called event streams. Instead of thinking of data as fixed instances, we treat them as events that occur over time. This allows us to incorporate time into our datasets, even if they’re not naturally ordered or time-series data. With event streams, we can modify, delete, filter outliers, and perform other operations on our training datasets reliably. Plus, we can seamlessly integrate new events and instances into our MLOps workflow.
You’ll learn how to:
- Improve the management of your model training process
- Store and access data using event streams
- Enhance reproducibility and reliability in your work
- Address issues related to dimensionality and model stability
No prior knowledge of streaming data or eventing systems is necessary for this beginner-friendly tutorial on discovering how event streams can revolutionize your data storage and model training.