Production-Grade ML & GenAI Operations — From Experimentation to Governed Deployment at Scale
An end-to-end MLOps and LLMOps platform built on Amazon SageMaker, Amazon Bedrock, and AWS-native CI/CD. Models are versioned, pipelines are automated, environments are isolated, and every artifact is auditable. Designed for enterprises that need to operationalize ML and GenAI without sacrificing governance or velocity.
A fully operational ML/GenAI platform with experimentation, training, deployment, and monitoring baked in.
SageMaker Studio with VPC-isolated notebooks, shared experiment tracking, and IdP-federated access
SageMaker Pipelines for automated preprocessing, training, evaluation, and model registration — DAG-orchestrated, fully reproducible
Centralized versioning, approval workflows, lineage tracking, and metadata
Amazon Bedrock integration for foundation model access, fine-tuning, RAG orchestration, and prompt management
Real-time endpoints, batch transform, and serverless inference with blue/green, canary, and A/B strategies
SageMaker Model Monitor for data drift, model quality, bias detection, and feature attribution
VPC endpoints, IAM least-privilege, KMS encryption at rest and in transit, network isolation per environment
CodePipeline / GitHub Actions with policy-as-code gates, automated testing, and multi-environment promotion
Everything is Infrastructure-as-Code. No model reaches production without passing through the pipeline.
Multi-account isolation — data scientists experiment freely, production is locked down.
Ingested and curated datasets, AWS Glue Data Catalog, Lake Formation governance, SageMaker Feature Store
SageMaker Studio notebooks, sandbox datasets (anonymized if GDPR), internet-enabled for framework exploration
ML pipeline development, SageMaker Pipelines, model training on production data (read-only), no console access
Code repositories, CI/CD pipelines, Model Registry, Amazon ECR for custom containers, artifact storage
Mirror of production — integration tests, stress tests, ML-specific validation (accuracy, latency, fairness)
Live inference endpoints, batch scoring, event-driven triggers, model monitoring, rollback mechanisms
Automated, reproducible, auditable — from notebook to production endpoint.
First-class support for foundation models — prompt versioning, RAG, evaluation, and cost tracking built in.
Discovery to first model in production in 6 weeks. LLMOps layer operational in parallel.
Stakeholder workshop, ML/GenAI use-case inventory, data readiness assessment, maturity evaluation, compliance requirements
SageMaker Studio setup, VPC isolation, IdP federation, notebook templates, data lake connectivity
SageMaker Pipelines for first use case, model registry, training and batch inference pipelines, baseline metrics
CodePipeline/GitHub Actions, pre-prod and prod accounts, automated testing, deployment strategies, rollback
Bedrock access, RAG pipeline, prompt management, guardrails, evaluation framework, cost tracking
Model Monitor, drift detection, alerting, SageMaker Project templates for new use-case onboarding, team self-service
Runbooks, cost optimization (Savings Plans, spot training, right-sizing), quarterly model audits, new use-case acceleration
Discovery to first model in production in 6 weeks.
Unified platform for experimentation, training, deployment, and monitoring — not a patchwork of tools
Managed foundation models with enterprise guardrails, no infrastructure to manage for LLM inference
Data scientists experiment freely, production is locked down, audit trails are immutable
Every model goes through automated pipelines, every deployment is tested, every artifact is versioned
Prompt versioning, RAG orchestration, LLM evaluation, and cost tracking are first-class citizens
SageMaker Project templates let new teams go from zero to production pipeline in days, not weeks
Built for organizations operationalizing machine learning and generative AI where model governance, reproducibility, and auditability are requirements — not nice-to-haves.