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Enterprise AI/ML Checklist

A comprehensive checklist for developing and deploying enterprise-grade AI/ML solutions, focusing on data management, model development, MLOps, monitoring, and ethical considerations. This checklist covers essential machine learning practices while embracing modern AI/ML architectures and methodologies.

Required

Suggested

Data Management

Required

Data Pipeline
Implement robust data ingestion and preprocessing pipelines.
Data Quality
Implement data quality checks and validation procedures.
Data Versioning
Implement data versioning and lineage tracking.
Data Privacy
Ensure data privacy and compliance with regulations.

Suggested

Data Catalog
Implement data catalog and discovery tools.
Data Augmentation
Implement data augmentation strategies where applicable.

Model Development

Required

Model Architecture
Define and document model architecture and design decisions.
Training Pipeline
Implement reproducible model training pipeline.
Model Validation
Implement comprehensive model validation procedures.
Model Versioning
Implement model versioning and artifact management.

Suggested

AutoML
Implement AutoML capabilities for model optimization.
Transfer Learning
Leverage transfer learning where applicable.

MLOps

Required

CI/CD Pipeline
Implement ML-specific CI/CD pipeline.
Model Registry
Implement model registry and deployment tracking.
Model Serving
Implement scalable model serving infrastructure.
Monitoring Setup
Implement model monitoring and alerting.

Suggested

Feature Store
Implement feature store for feature management.
A/B Testing
Implement A/B testing framework for models.

Model Monitoring

Required

Performance Monitoring
Monitor model performance and accuracy metrics.
Drift Detection
Implement data and concept drift detection.
Resource Monitoring
Monitor computational resource usage and costs.
Alert System
Implement alerting system for model issues.

Suggested

Automated Retraining
Implement automated model retraining triggers.
Performance Dashboard
Create comprehensive model performance dashboards.

Ethics & Fairness

Required

Bias Detection
Implement bias detection and mitigation strategies.
Fairness Metrics
Define and monitor fairness metrics.
Model Explainability
Implement model explainability techniques.
Ethics Guidelines
Establish AI ethics guidelines and review process.

Suggested

Ethics Board
Establish AI ethics review board.
Impact Assessment
Conduct regular AI impact assessments.

Documentation

Required

Model Cards
Create comprehensive model cards for all models.
Data Documentation
Document data sources, schemas, and transformations.
Pipeline Documentation
Document ML pipelines and deployment processes.
API Documentation
Create comprehensive API documentation for model endpoints.

Suggested

Decision Records
Maintain ML architecture decision records.
Experiment Tracking
Document and track ML experiments and results.

Security & Compliance

Required

Model Security
Implement model security measures and access controls.
Data Security
Ensure data security throughout ML pipeline.
Compliance Checks
Implement ML-specific compliance checks and audits.
Privacy Protection
Implement privacy-preserving ML techniques.

Suggested

Attack Prevention
Implement adversarial attack prevention measures.
Security Testing
Conduct regular ML security testing and audits.