Standard 4 stops to get here · leads to 5

MLOps

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

Your route here

4 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 · you are here ✓ understood

Where it sits

Before this

Model Serving
MLOps

Leads to

Data Versioning Model Registry Model Versioning CI/CD for ML Feature Store

Explore nearby

Shipping AI Model Monitoring Tracking model performance, data distribution, and predictions in production to detect issues and degradation. Shipping AI CI/CD for ML Continuous integration and deployment practices adapted for machine learning pipelines. 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 Feature Store A centralized platform for managing, storing, and serving features for ML models. Shipping AI Data Versioning Tracking different versions of datasets to ensure reproducibility and manage changes.