Let’s take a dive into ML systems for ranking and search relevance on architectures such as Elasticsearch and vector databases like Pinecone and what it means to monitor them for quality, edge cases, and corrupt data.

We’ll take a hands-on dive into implementing the 1st level of MLOps maturity and performing continuous training of the model by automating our ML pipeline. We’ll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.