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CCC Machine Learning Development Environment

Machine Learning Development Environment refers to the suite of tools, infrastructure, and processes that facilitate the development, testing, deployment, and maintenance of machine learning models.

Release Details

Version:
DEV
Assurance Level:
Release Manager:
DB
Development Build

Contributors

DT
Development Team

Change Log

  • Development build - no formal changelog available

Capabilities

IDTitleDescriptionThreat Mappings
CCC.MLDE.CP01Managed Notebook EnvironmentsProvides fully managed notebook instances specifically designed for machine learning development, eliminating the need to manage underlying infrastructure.
0
CCC.MLDE.CP02Pre-configured Machine Learning LibrariesOffers environments pre-installed with popular machine learning libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn, optimized for ML tasks.
0
CCC.MLDE.CP03Integrated Experiment ManagementFacilitates tracking and management of machine learning experiments, including parameters, metrics, and artifacts, within the development environment.
0
CCC.MLDE.CP04Model Training and Deployment IntegrationSupports seamless transition from model development to training and deployment, allowing models to be trained and deployed directly from the MLDE.
0
CCC.MLDE.CP05Automated Machine Learning (AutoML) CapabilitiesOffers AutoML functionalities to automatically build, train, and optimize machine learning models with minimal manual intervention.
0
CCC.MLDE.CP06GPU/Specialized Hardware SupportProvides access to GPU instances and specialized ML acceleration hardware (TPUs, FPGAs) with automated driver and runtime management.
0
CCC.MLDE.CP07Data Pipeline IntegrationSupports integration with data preparation and feature engineering pipelines, including versioning of datasets and capabilities used in ML experiments.
0
CCC.MLDE.CP08Model RegistryProvides centralized storage and versioning for trained models, including metadata about training runs, model artifacts, and deployment history.
0
CCC.MLDE.CP09Collaborative Development SupportEnables multiple data scientists to work on the same project with version control integration, shared notebooks, and resource management.
0
CCC.MLDE.CP10Model Monitoring and Drift DetectionSupports monitoring of deployed models for performance degradation, data drift, and concept drift with automated alerting capabilities.
0
CCC.MLDE.CP11Reproducibility CapabilitiesProvides capability to capture and version all components needed to reproduce an ML experiment, including code, data, and environment configurations.
0
CCC.MLDE.CP12Resource Scheduling and OptimizationSupports scheduling and optimization of compute resources for training jobs, including spot instance usage and auto-scaling capabilities.
0
CCC.MLDE.CP13Security and Compliance ControlsProvides specific controls for ML workflows including model governance, bias detection, and compliance documentation for regulated industries.
0
CCC.Core.CP03Access Log PublicationThe service automatically publishes structured, verbose records of activities performed within the scope of the service by external actors.
0
CCC.Core.CP06Access ControlThe service automatically enforces user configurations to restrict or allow access to a specific component or a child resource based on factors such as user identities, roles, groups, or attributes.
0
CCC.Core.CP08Data ReplicationThe service automatically replicates data across multiple deployments simultaneously with parity, or may be configured to do so.
0
CCC.Core.CP09Metrics PublicationThe service automatically publishes structured, numeric, time-series data points related to the performance, availability, and health of the service or its child resources.
0
CCC.Core.CP10Log PublicationThe service automatically publishes structured, verbose records of activities, operations, or events that occur within the service.
0
CCC.Core.CP14API AccessThe service exposes a port enabling external actors to interact programmatically with the service and its resources using HTTP protocol methods such as GET, POST, PUT, and DELETE.
0
CCC.Core.CP15Cost ManagementThe service monitors data published by child or networked resources to infer usage patterns and generate cost reports for the service.
0
CCC.Core.CP16BudgetingThe service may be configured to take a user-specified action when a spending threshold is met or exceeded on a child or networked resource.
0
CCC.Core.CP17AlertingThe service may be configured to emit a notification based on a user-defined condition related to the data published by a child or networked resource.
0
CCC.Core.CP20Resource TaggingThe service provides users with the ability to tag a child resource with metadata that can be reviewed or queried.
0
CCC.Core.CP23Network Access RulesThe service restricts access to child or networked resources based on user-defined network parameters such as IP address, protocol, port, or source.
0

Controls

IDTitleObjectiveControl FamilyThreat MappingsGuideline MappingsAssessment Requirements
CCC.Core.CN01Encrypt Data for TransmissionEnsure that all communications are encrypted in transit to protect data integrity and confidentiality. Data
1
4
5
CCC.Core.CN02Encrypt Data for StorageEnsure that all data stored is encrypted at rest using strong encryption algorithms. Data
1
4
1
CCC.Core.CN03Implement Multi-factor Authentication (MFA) for AccessEnsure that all sensitive activities require two or more identity factors during authentication to prevent unauthorized access. Identity and Access Management
1
1
4
CCC.Core.CN04Log All Access and ChangesEnsure that all access attempts are logged to maintain a detailed audit trail for security and compliance purposes. Logging & Monitoring
1
1
3
CCC.Core.CN05Prevent Access from Untrusted EntitiesEnsure that secure access controls enforce the principle of least privilege to restrict access to authorized entities from explicitly trusted sources only. Identity and Access Management
1
5
6
CCC.Core.CN06Restrict Deployments to Trust PerimeterEnsure that the service and its child resources are only deployed on infrastructure in locations that are explicitly included within a defined trust perimeter. Data
1
1
2
CCC.MLDE.CN01Define Access Mode for ML Development EnvironmentsEnsure that access to Machine Learning Development Environment (MLDE) resources is strictly defined and controlled. Only authorized users with appropriate permissions can access these environments, mitigating the risk of unauthorized access, data leakage, or service disruption. Identity and Access Management
2
7
1
CCC.MLDE.CN02Disable File Downloads on MLDE InstancesPrevent unauthorized file downloads from MLDE instances to protect sensitive data from being exfiltrated. Data Protection
2
6
2
CCC.MLDE.CN03Disable Root Access on MLDE InstancesPrevent users from obtaining root access on MLDE instances to reduce the risk of unauthorized system modifications and potential security breaches. Identity and Access Management
1
5
2
CCC.MLDE.CN04Disable Terminal Access on MLDE InstancesPrevent users from accessing the terminal on MLDE instances to limit the risk of unauthorized commands and potential system compromise. Identity and Access Management
1
4
2
CCC.MLDE.CN05Restrict Environment Options on MLDE InstancesLimit the virtual machine and container image options available when creating new MLDE instances to approved and secure configurations. Configuration Management
1
4
2
CCC.MLDE.CN06Require Automatic Scheduled Upgrades on User-Managed MLDE InstancesEnsure that MLDE instances are kept up-to-date with the latest security patches by enforcing automatic scheduled upgrades. Vulnerability Management
2
5
2
CCC.MLDE.CN07Restrict Public IP Access on MLDE InstancesPrevent public IP access to MLDE instances to reduce exposure to the internet and enhance security. Network Security
2
4
2
CCC.MLDE.CN08Restrict Virtual Networks for MLDE InstancesLimit the virtual networks that can be used when creating new MLDE instances to ensure they are deployed within approved and secure network environments. Network Security
2
4
2