Reference 5 stops to get here

CI/CD for ML

Continuous integration and deployment practices adapted for machine learning pipelines.

Your route here

5 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Inference ✓ understood

    Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

  4. Model Serving ✓ understood

    Deploying trained models as services that can handle prediction requests in production environments.

  5. MLOps ✓ understood

    Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.

  6. CI/CD for ML · you are here ✓ understood

Continuous integration and deployment practices adapted for machine learning pipelines.

This concept is essential for understanding practical deployment and forms a key part of modern AI systems.

  • MLOps
  • Automation
  • Deployment

Where it sits

Before this

MLOps
CI/CD for ML

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Model Registry A centralized repository for tracking, versioning, and managing trained models. Shipping AI Experiment Tracking Recording hyperparameters, metrics, and artifacts from training runs for comparison and reproducibility. Shipping AI Model Retraining Periodically updating models with new data to maintain performance as distributions change. Shipping AI Model Serving Deploying trained models as services that can handle prediction requests in production environments.