MLOps with MLflow on Kraken CI

MLOps and MLflow

  • Tracking experiments to record and compare parameters and results
    (MLflow Tracking).
  • Packaging ML code in a reusable, reproducible form to share
    with other data scientists or transfer to production (MLflow
    Projects).
  • Managing and deploying models from various ML libraries to a
    variety of model serving and inference platforms (MLflow Models).
  • Providing a central model store to collaboratively manage the entire
    lifecycle of an MLflow Model, including model versioning, stage
    transitions, and annotations (MLflow Model Registry).

MLflow in Kraken CI

  1. pulling live stock data and preparing it for training (source 1, source 2)
  2. performing the training (source 3)
  3. storing model metrics in Kraken CI for charting

Workflow Definition

  1. Checkout mflow example project sources
  2. Run the mlflow project ie. download data, prepare it, run a
    training and at the end store metrics about the trained model to
    metrics.json
  3. Upload collected metrics together with hyperparameters from
    params.json to Kraken server

Execution and Monitoring

Summary

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Michal Nowikowski

Michal Nowikowski

Kraken CI Founder. I’m software engineer focused on full-stack programming and improving software processes.