AWS Data & ML Architectures - Pipelines, MLOps & Real-Time AI
Learn AWS Data & Machine Learning reference architectures, including MLOps platforms, streaming CDC pipelines, GPU training clusters, serverless AI inference, and vision processing. Fully open-source with IaC, diagrams, deployment guides, and best practices.
Modern AI workloads need scalable pipelines, training infrastructure, experiment tracking, and real-time inference engines.
These open-source reference architectures help teams build full lifecycle ML solutions using AWS best practices.
All repositories are fully documented, including IaC templates, cost estimates, and production patterns.
Data & ML Projects
| Project | Description | Stack |
|---|---|---|
| mlops-full-lifecycle-platform | End-to-End ML Platform with model versioning | SageMaker + MLflow |
| distributed-deep-learning-cluster | Multi-GPU Training Cluster | EKS + GPU + Horovod |
| change-data-capture-streaming | Real-Time CDC with Kafka & Kinesis | DMS + Kafka + Kinesis |
| serverless-data-pipeline-lakehouse | Glue + Athena + Delta Lake | Glue + Athena |
| computer-vision-pipeline-infrastructure | Real-Time Video Analytics | Rekognition + Kinesis |
| infratales-serverless-ai-inference | Serverless AI/ML Inference at Scale | Lambda + SageMaker |
| graph-database-knowledge-graph | Neptune Graph Database for Knowledge Graphs | Neptune + Gremlin |
1. mlops-full-lifecycle-platform
InfraTalesStack: SageMaker • MLflow • ECR • S3
Highlights
- Model training, deployment, tracking
- CI/CD for ML models
- Feature store + versioning
Perfect For
✔ AI product teams
✔ ML Ops automation
2. distributed-deep-learning-cluster
InfraTalesStack: EKS • GPU Nodes • Horovod
Features
- Distributed training jobs
- Auto-scaling GPU workers
- Parallel deep learning workloads
3. change-data-capture-streaming
InfraTalesStack: DMS • Kafka • Kinesis
Purpose
Real-time replication from DB → Events Pipeline.
Use Cases
- analytics dashboards
- microservices sync
4. serverless-data-pipeline-lakehouse
InfraTalesStack: Glue • Athena • Delta Lake
Benefits
- No servers to manage
- Pay only per query
- Ideal for ETL + analytics
5. computer-vision-pipeline-infrastructure
InfraTalesStack: Rekognition • Kinesis
Capabilities
- Live video ingestion
- Frame analytics + inference
6. infratales-serverless-ai-inference
InfraTalesStack: Lambda + SageMaker
Highlights
- Low latency inference at scale
- Cold start optimized
- Multi-model endpoints
7. graph-database-knowledge-graph
InfraTalesStack: Neptune + Gremlin
Use Case
Knowledge graphs, recommendations & relationships.
These architectures help developers deploy end-to-end ML systems, from pipelines to inference, quickly using real-world, tested blueprints.
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Rahul Ladumor
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Rahul Ladumor