HelixML for Machine Learning Model Development

Scenario: A developer, named Alex, is looking for an efficient and flexible solution for machine learning model development. HelixML is an open-source machine learning platform built using Helix, a lightweight and extensible framework, that provides a complete workflow for data processing, model training, and inference. HelixML supports various machine learning algorithms and deep learning frameworks, making it an ideal choice for Alex.

To understand HelixML and its capabilities, Alex decides to follow these steps:

  1. Familiarize with HelixML:

  2. Understand HelixML architecture:

    • Explore the HelixML codebase:
      • api/: Contains the main HelixML API and its related components.
      • charts/: Contains Helm charts for deploying HelixML.
      • cog/: Contains Cog, a HelixML extension for model serving.
      • demos/: Contains example projects for using HelixML.
      • docs/: Contains documentation for HelixML.
      • frontend/: Contains the HelixML frontend.
      • llamaindex/: Contains the LLM indexing service.
      • runner/: Contains the HelixML runner for distributed training.
      • scripts/: Contains various scripts for HelixML.
      • Dockerfile, Dockerfile.api, Dockerfile.demos, Dockerfile.runner: Contain Dockerfile configurations for HelixML components.
  3. Set up HelixML environment:

  4. Prepare data:

  5. Develop machine learning models:

  6. Test machine learning models:

  7. Deploy machine learning models:


  1. Verify that HelixML is installed correctly by running the API server.
  2. Verify that data can be preprocessed and stored using HelixML.
  3. Verify that machine learning models can be developed, trained, and fine-tuned using HelixML.
  4. Verify that machine learning models can be tested using HelixML.
  5. Verify that machine learning models can be deployed using HelixML and ClearML.
  6. Verify that machine learning models can be monitored using ClearML.