AWS AI/MLOps Foundation Consulting

OVERVIEW

Transform your ML operations with Virtuability’s enterprise-grade MLOps foundation and governance framework.

Virtuability, as a leading AWS Services Partner, specialises in delivering a comprehensive MLOps foundation that enables organisations to scale their machine learning initiatives with confidence. We leverage AWS SageMaker, Bedrock, Control Tower and multi-account architectures to create robust, governed ML environments for Data Scientists and MLOps Engineers. Our approach combines deep technical expertise with proven governance frameworks to deliver automated deployment pipelines, centralised model registries and enterprise-grade security controls. We implement AWS Well-Architected principles specifically for the ML environment and workloads, ensuring your AI initiatives are scalable, secure and operationally efficient while maintaining compliance.

Capabilities

Multi-Account MLOps Architecture

Virtuability designs and implements multi-account structures using AWS Control Tower and AWS Organizations, providing clear separation between data science environments, shared services, training and production workloads. This architecture ensures secure, scalable ML operations with proper governance boundaries and cost allocation.

Automated ML Pipelines

We deliver end-to-end automation using AWS CDK Pipelines and AWS SageMaker, creating standardised deployment processes for models. Our pipeline factory approach enables rapid onboarding of new models while maintaining consistency and governance.

Centralised Model Governance

Virtuability implements centralised model registries and governance frameworks using AWS SageMaker Model Registry and guardrails. This helps model lineage tracking, governance and versioning across environments.

Security & Compliance Controls

We implement comprehensive security controls for training and other data, encryption at rest and in transit, IAM permission boundaries and custom guardrails. These controls ensure your ML workloads meet enterprise security standards and regulatory requirements.

Benefits

Why Build an MLOps Foundation?

Accelerated Model Deployment

Transform your model training and deployment process from manual, error-prone workflows to fully automated pipelines. Our MLOps foundation approach reduces deployment time while improving reliability and consistency. Automated testing and validation ensure only high-quality models reach production, enabling faster innovation cycles and competitive advantage.

Enterprise Governance

Implement comprehensive governance controls that scale with your organisation. Our multi-layered approach includes Control Tower guardrails, Service Control Policies and CDK guardrails to ensure compliance, cost control and security.

Operational Excellence

Achieve operational excellence through standardised processes, centralised monitoring and automated resource management. Our embedded consulting approach ensures knowledge transfer and capability building within your team, creating sustainable operational practices that reduce long-term dependency while maximising efficiency.

Cost Optimisation

Implement sophisticated cost allocation strategies through mandatory tagging, environment-specific resource sizing and lifecycle management. Our framework enables granular cost tracking by project while optimising resource utilisation across development, testing and production environments.

AWS SageMaker and integrated MLOps services

Machine Learning at enterprise scale requires sophisticated orchestration and governance. At Virtuability, we leverage a comprehensive suite of AWS AI/ML services to build robust, scalable MLOps foundations that enable your organisation to innovate with confidence.

AWS SageMaker: Complete ML Platform

AWS SageMaker provides a fully managed platform for the entire machine learning lifecycle. We configure SageMaker Domains in VPC-only mode with encryption, implement SageMaker projects for standardised workflows and leverage SageMaker Model Registry for centralised model management and lineage tracking.

AWS Control Tower: Multi-Account Governance

AWS Control Tower simplifies the setup of secure, multi-account ML environments. We implement proactive and preventive controls specific to ML workloads, ensuring VPC requirements, encryption standards and resource tagging are automatically enforced across all ML accounts.

AWS CDK & Pipelines: Infrastructure as Code

The AWS Cloud Development Kit enables us to define ML infrastructure using familiar programming languages. CDK Pipelines can automate the deployment of ML shared services, registries and models across multiple environments through Cloudformation, while our custom CDK guardrails enforce security and governance standards both before and at deployment time.

AWS Bedrock: Foundation Model Access

AWS Bedrock provides secure access to foundation models through managed APIs. We implement VPC endpoints for private access, configure appropriate IAM policies for controlled access and integrate Bedrock models into your ML workflows while maintaining security and governance standards.

AWS Lambda, API Gateway and AppSync: Model Serving

We can create consistent model access patterns such as AWS Lambda and API Gateway or AppSync, to abstract underlying SageMaker endpoints or preventing direct access. This approach can enable dynamic model registration, versioning and load balancing while providing unified access across different model types and environments.

AWS Organizations & IAM: Security Framework

AWS Organizations and IAM provide the security foundation for multi-account ML operations. We implement least-privilege access patterns and permission boundaries for ML workloads.

Monitoring & Observability: Visibility

Comprehensive monitoring using AWS CloudWatch, AWS CloudTrail, Sagemaker and custom dashboards can provide visibility into ML operations.

AWS AI/MLOps Foundation
Train with Confidence