Results for “dml”

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

YAML configuration for CI/CD, Docker Compose, and Kubernetes.

Deploy a trained model to serving with versioning, shadow or canary rollout, and a tested rollback path.

Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.

Monitors Datadog Service Level Objectives and burn rate alerts via the Datadog API v2. Generates SLO compliance reports and triggers remediation workflows when error budgets are exhausted.