AI/ML Operations

To deploy machine learning models as scalable serving endpoints, you can launch TensorFlow Serving or Triton containers to expose inference APIs. This Skill provides example deployments and patterns for configuring these serving frameworks within your infrastructure.

The best way to track ML experiments and manage model versions is using MLflow for experiment tracking alongside a model registry. This Skill specifies requirements for integrating MLflow to monitor performance, manage versions, and trigger retraining workflows.

To set up a feature store for machine learning workflows, you can use Feast to manage and serve features consistently across training and inference. This Skill provides reference implementations for integrating Feast into your end-to-end MLOps pipeline.

Yes, you can use this for GPU-enabled ML model serving infrastructure. The Skill specifies requirements for GPU-enabled environments and includes example deployment patterns to optimize model inference using Triton or TensorFlow Serving.

MLOps handles model drift monitoring and governance by tracking performance metrics, detecting data drift, and establishing retraining triggers. This Skill helps implement monitoring workflows to maintain model accuracy and govern deployed versions effectively.

You need an end-to-end MLOps pipeline from data ingestion to deployment when operationalizing machine learning models at scale. This Skill orchestrates the entire workflow including feature engineering, training, evaluation, registry, and serving to solve deployment and governance challenges.