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MLOps with MLflow on Kraken CI

· 3 min read
Michal Nowikowski

Besides building, testing and deploying, Kraken CI is also a pretty nice tool to build an MLOps pipeline. In this article, it will be shown how to leverage Kraken CI to build a CI workflow for machine learning using MLflow.

MLOps and MLflow

MLOps is a set of practices that aims to build and maintain machine learning models in production reliably and efficiently. One of prominent tools in this area is MLflow.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:

  • 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

In the following sections, I will describe how to prepare a workflow in Kraken CI to train an ML model. This is an LSTM model that will predict stock prices based on historical data.

The workflow will be:

  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

The MLflow project is described in MLproject.

Workflow Definition

The whole Kraken CI workload is defined here.

There are 3 steps:

   "steps": [{
"tool": "git",
"checkout": ""
}, {
"tool": "shell",
"cmd": "/opt/conda/bin/mlflow run .",
"cwd": "mlflow-example",
"timeout": 1200
}, {
"tool": "values_collect",
"files": [{
"name": "metrics.json",
"namespace": "metrics"
}, {
"name": "params.json",
"namespace": "params"
"cwd": "mlflow-example"
  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

The last step allows for charting accuracy and RMS of the model over builds.

There is one more element defined in the workflow: the definition of execution environment:

   "environments": [{
"system": "krakenci/mlflow",
"executor": "docker",
"agents_group": "all",
"config": "default"

Here we can notice the use of a pre-prepared image with mlflow. It is available in Docker hub: krakenci/mlflow.

The whole example of workflow is present in Kraken lab: Check the steps definitions in branch management page.

Execution and Monitoring

Besides the workflow definition, Kraken UI also shows collected data and the charts drawn from this data:, the charts tab.

The right chart shows value of loss collected over time:


This article shows how Kraken CI can be used to build an MLOps pipeline. The pipeline downloads raw data, prepares the data for training and then executes the training. The trained model metrics are collected and charted in Kraken UI at the end.