How to log model artifacts with MLFLOW and DVC?

• mlem

  18 704 8.2 Python

  Discontinued 🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

  

Here are a few things to consider: 1. You're using both DVC and MLflow to store the model artifact, why? 2. How I envision MLflow, DVC and git to work together is like this. DVC to manage the training dataset, git to manage the code, and MLflow will do the rest. About the part about "versioning" the model, MLflow has a model registry feature to "tag" a well-performing experiment. 3. Or just to do everything in DVC. DVC also has a way to do experiment tracking. Then if you need a model registry there's MLEM by the same company.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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