AI Readiness Assessment for AWS

Compute Readiness

Assessing compute readiness for AI workloads on AWS begins with understanding your inference and training requirements. For real-time inference, evaluate whether your current EC2 instance types and Auto Scaling configurations can handle the latency and throughput demands of ML model serving. Consider GPU-optimized instances like the P4d and G5 families for compute-intensive workloads.

Review your container orchestration strategy, as most modern AI deployments leverage Amazon ECS or EKS for model serving. Ensure your clusters have appropriate node groups configured for GPU workloads and that your scaling policies account for the bursty nature of AI inference traffic.

For batch processing and model training, evaluate your use of Amazon SageMaker, including managed training jobs, processing jobs, and hyperparameter tuning. Organizations that have not yet adopted SageMaker should assess the migration effort from custom training pipelines, as the managed service typically reduces operational overhead by fifty percent or more.

Data Readiness

Data readiness is often the most critical and overlooked dimension of AI preparation. Audit your data lake architecture on Amazon S3, ensuring that raw, curated, and feature-ready data tiers are clearly separated with appropriate access controls. Evaluate your data catalog completeness in AWS Glue and verify that schema evolution is handled gracefully across your pipelines.

Assess data quality and freshness across your key datasets. AI models are only as reliable as the data they consume, so establish automated data quality checks using AWS Glue Data Quality or custom validation steps in your ETL pipelines. Pay particular attention to feature drift detection, which can silently degrade model performance over time.

Governance Readiness

AI governance on AWS requires a comprehensive approach spanning model versioning, access control, audit logging, and bias monitoring. Evaluate your current IAM policies to ensure least-privilege access to training data, model artifacts, and inference endpoints. Implement AWS CloudTrail logging for all SageMaker API calls to maintain a complete audit trail.

Establish a model registry using SageMaker Model Registry to track model lineage, approval workflows, and deployment history. This provides the traceability required for regulatory compliance and enables rapid rollback when model performance degrades in production.

Finally, assess your organization's readiness for responsible AI practices. This includes defining fairness metrics for your use cases, implementing bias detection using SageMaker Clarify, and establishing review processes for high-stakes AI decisions. Document these practices in a governance framework that can scale as your AI footprint grows.