Virtuability is proud to offer a comprehensive AWS Well-Architected Review with a focus on AI/ML services such as Sagemaker and Bedrock. Our team of certified AWS experts will work with you to assess your current architecture and provide recommendations for improvement based on the latest AWS prescriptive guidance.

Our offering includes a detailed review of your account structure & landing zone, governance & guardrails, model training environment and deployment automation. We will help you identify potential problem areas and suggest improvements to ensure that your AI/ML model training environments & workloads are secure, reliable and cost-effective.

By adopting the well-architected review, customers can benefit from:

• Accelerating AI/ML adoption across the organization while mitigating risks
• Optimizing the setup and governance of secure, scalable, and reliable AI/ML environments
• Enabling collaboration and reuse of data and features across AI/ML projects
• Optimising costs and improving efficiency of AI/ML operations

With Virtuability, you can be confident that your AI/ML workloads are designed and operated according to the highest standards. Contact us today to learn more about our AWS Well-Architected Review offering and how we can help you achieve your goals.

Key features

Multi-Account Structure: We will review your current account structure, landing zone, AI/ML shared services and security configuration and provide recommendations for improvement to ensure that your AI/ML workloads are secure and well-organized.

Governance & Guardrails: We will review your governance & guardrail policies and provide recommendations for improvement to ensure that your AI/ML workloads are compliant with industry standards and best practices.

Model Training Environment: We will assess your AI/ML model training & data science environment and training data storage and provide suggestions for optimization to ensure that your AI models are trained effectively and securely.

Deployment Automation: We will assess your deployment automation processes for models & endpoints and provide suggestions for optimization to ensure that your AI/ML workloads are deployed quickly and reliably.

Areas of Focus

• Multi-account structure and strategy
• Governance & Guardrails
• Model training environment
• Deployment automation
• Security