AI/ML / Mlde

Capabilities

Version:

ID	Title	Description
CCC.MLDE.CP01	Managed Notebook Environments	Provides fully managed notebook instances specifically designed for machine learning development, eliminating the need to manage underlying infrastructure.
CCC.MLDE.CP02	Pre-configured Machine Learning Libraries	Offers environments pre-installed with popular machine learning libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn, optimized for ML tasks.
CCC.MLDE.CP03	Integrated Experiment Management	Facilitates tracking and management of machine learning experiments, including parameters, metrics, and artifacts, within the development environment.
CCC.MLDE.CP04	Model Training and Deployment Integration	Supports seamless transition from model development to training and deployment, allowing models to be trained and deployed directly from the MLDE.
CCC.MLDE.CP05	Automated Machine Learning (AutoML) Capabilities	Offers AutoML functionalities to automatically build, train, and optimize machine learning models with minimal manual intervention.
CCC.MLDE.CP06	GPU/Specialized Hardware Support	Provides access to GPU instances and specialized ML acceleration hardware (TPUs, FPGAs) with automated driver and runtime management.
CCC.MLDE.CP07	Data Pipeline Integration	Supports integration with data preparation and feature engineering pipelines, including versioning of datasets and capabilities used in ML experiments.
CCC.MLDE.CP08	Model Registry	Provides centralized storage and versioning for trained models, including metadata about training runs, model artifacts, and deployment history.
CCC.MLDE.CP09	Collaborative Development Support	Enables multiple data scientists to work on the same project with version control integration, shared notebooks, and resource management.
CCC.MLDE.CP10	Model Monitoring and Drift Detection	Supports monitoring of deployed models for performance degradation, data drift, and concept drift with automated alerting capabilities.
CCC.MLDE.CP11	Reproducibility Capabilities	Provides capability to capture and version all components needed to reproduce an ML experiment, including code, data, and environment configurations.
CCC.MLDE.CP12	Resource Scheduling and Optimization	Supports scheduling and optimization of compute resources for training jobs, including spot instance usage and auto-scaling capabilities.
CCC.MLDE.CP13	Security and Compliance Controls	Provides specific controls for ML workflows including model governance, bias detection, and compliance documentation for regulated industries.