ClearML

Configure ClearML to track training experiments.

Utilize hyperparameter optimization to find optimal training parameters.

Leverage the Infrastructure Control Plane to dynamically allocate GPU resources.

Monitor training progress in real-time using the AI Development Center.

Deploy the trained model using the GenAI App Engine.

Ingest data and create a vector database.

Fine-tune an off-the-shelf LLM.

Launch the LLM API on compute clusters using the GenAI App Engine.

Implement access control and monitoring.

Gather feedback for continuous improvement.

Configure multi-tenancy in the Infrastructure Control Plane.

Implement role-based access control.

Set up quota management for each tenant.

Monitor resource utilization and generate usage-based chargebacks.

Ensure isolated networks and storage for each tenant.

Integrate ClearML with your existing CI/CD pipeline.

Use the REST API to trigger CI/CD actions based on model performance.

Automate continuous training actions.

Monitor model performance in production.

Retrain and redeploy models as needed.

Use ClearML to track experiment parameters and metrics.

Automatically log command-line arguments and console output.

Share direct links to experiments with colleagues.

Create and share reports within the platform.

Organize experiments into projects and sub-projects.