We build on MLOps pipelines that are optimized for scale, efficiency, and control. We rely on common Python frameworks (Pandas, Spark) to enable (big) data analysis and features engineering, that are sunk in our feature store.
For model building and training we rely on state-of-the-art libraries such as PyTorch, scikit-learn or ONNX. Every experiment and model deployment is tracked by MLFLow, which allows us to use a fine model selection and standardized offline validation procedure.
Finally, we rely on a complete observability platform powered by Azure Monitor and Grafana that monitors our deployed APIs, providing all the insights necessary for online validation and A/B testing.
