Results for “model-checking”

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Automates Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

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

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Mandatory training archive with model gating (APPROVED/REVIEW/DROP). Trigger when: (1) training run completes, (2) need to decide which models to deploy, (3) want historical training reference, (4) need checkpoint recommendations for overfitting.

Sets up, manages, and executes queries against Cloud Firestore database instances. You MUST unconditionally activate this skill if you plan to use Firestore in any way. Use when listing or creating Firestore databases, configuring security rules, designing data models, writing client SDK queries, or checking indexes.