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Release · v2026.06-rc4

FINOS-CCC/CCC.MLDE.CP Capability Catalog

FINOS-CCC/CCC.MLDE.CP

Capabilities for Machine Learning Development Environment technologies, as defined by the FINOS Common Cloud Controls project.

Published by FINOS Common Cloud Controls

Install

OCI v1.1
$grcli unpack --repository finos-ccc/ccc.mlde.cp --tag v2026.06-rc4
Coordinate
oci.grc.store/finos-ccc/ccc.mlde.cp:v2026.06-rc4
Manifest digest
sha256:f8ff7b79798e3ef01a21fe929a96f96c368ecc36b1b6ae142df6b43b50e2c85f

Provenance

1 layer
Digest Media type Size
aa247674a3af… application/vnd.gemara.artifact.v1+yaml 6.7 KiB
Bundle config blob 
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CCC Machine Learning Development Environment Capabilities

Capabilities for Machine Learning Development Environment technologies, as defined by the FINOS Common Cloud Controls project.

ID
CCC.MLDE.CP
Version
v2026.06-rc4
Gemara version
v1.2.0
Author
FINOS Common Cloud Controls

Machine Learning

The Machine Learning group covers entries related to building, training, deploying, and managing ML models and AI systems. This includes development environments, experiment tracking, model registries, inference, generative AI, prompt engineering, and model governance.

  1. CCC.MLDE.CP01 Managed Notebook Environments

    Provides fully managed notebook instances specifically designed for machine learning development, eliminating the need to manage underlying infrastructure.

  2. 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.

  3. CCC.MLDE.CP03 Integrated Experiment Management

    Facilitates tracking and management of machine learning experiments, including parameters, metrics, and artifacts, within the development environment.

  4. 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.

  5. CCC.MLDE.CP05 Automated Machine Learning (AutoML) Capabilities

    Offers AutoML functionalities to automatically build, train, and optimize machine learning models with minimal manual intervention.

  6. CCC.MLDE.CP08 Model Registry

    Provides centralized storage and versioning for trained models, including metadata about training runs, model artifacts, and deployment history.

  7. 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.

  8. 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.

  9. CCC.MLDE.CP13 Security and Compliance Controls

    Provides specific controls for ML workflows including model governance, bias detection, and compliance documentation for regulated industries.

Compute

The Compute group covers entries related to processing, execution, and runtime infrastructure. This includes CPU, memory, storage allocation, network ports, command-line interfaces, and elastic scaling.

  1. 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.

Data Processing

The Data Processing group covers entries related to transforming, enriching, and moving data through pipelines. This includes ETL/ELT, stream and batch processing, data lineage, schema evolution, and workflow orchestration for data workloads.

  1. 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.

Observability

The Observability group covers entries related to logging, monitoring, metrics, alerting, and event publication. This includes audit trail integrity, enumeration detection, and protection against tampering or unauthorized access to operational telemetry.

  1. 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.

Resource Management

The Resource Management group covers entries related to the lifecycle, configuration, and operational integrity of cloud resources. This includes resource exhaustion, tag manipulation, version rollback, scaling, and cost management.

  1. 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.