MLflow

About

MLflow is an open-source platform to manage the whole ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.

The MLflow adapter for CrateDB, available through the mlflow-cratedb package on PyPI, provides support to use CrateDB as a storage database for the MLflow Tracking subsystem, which is about recording and querying experiments, across code, data, config, and results.

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About using MLflow together with CrateDB.

Blog: Running Time Series Models in Production using CrateDB

Part 1: Introduction to Time Series Modeling using Machine Learning

The article will introduce you to the concept of time series modeling, discussing the main obstacles running it in production. It will introduce you to CrateDB, highlighting its key features and benefits, why it stands out in managing time series data, and why it is an especially good fit for supporting machine learning models in production.

Fundamentals
Time Series Modeling

Notebook: Create a Time Series Anomaly Detection Model

Guidelines and runnable code to get started with MLflow and CrateDB, exercising time series anomaly detection and time series forecasting / prediction using NumPy, Salesforce Merlion, and Matplotlib.

Fundamentals
Time Series
Anomaly Detection
Prediction / Forecasting