FLUID ML - Well-Architected Review For Machine Learning Workloads in AWS

Our FLUID assessment methodology offers a strategic and comprehensive approach designed to refine data and machine learning workloads, ensuring precise alignment with business objectives. By leveraging AWS services such as Amazon SageMaker, AWS Bedrock, and AWS Lambda, this methodology prioritizes strategic direction, investment alignment, adherence to best practices, and cost optimization to prevent inefficient resource allocation and "spray and pray" tactics from the outset.

With an emphasis on iterative improvement, FLUID maintains continuous momentum in project execution, ensuring that no constraints hinder sustained progress.

Each assessment is conducted by two AWS-certified Solution Architects to ensure an objective and comprehensive evaluation, reinforcing our commitment to delivering well-architected solutions that drive business success.

Delivery Mechanism:

The FLUID assessment results in a comprehensive deliverable package which includes:

• Generated PDF Report: Produced by our proprietary FLUID Assessment Tool, this report features a bespoke questionnaire tailored specifically for the company, combined with our expert analysis leveraging AWS AI and machine learning services.

• Executive Summary: A concise overview summarizing the current technology landscape of the company, highlighting strengths, weaknesses, and critical areas for improvement, with specific recommendations on AWS service optimization.

• Presentation Walk-through: Detailed presentation of the assessment findings to the business and/or technology teams, enhancing understanding and facilitating decision-making with AWS service insights.

• Traffic Light Analysis: A visual summary using a traffic light system to indicate the status and risk levels of various components within the technology landscape, with specific AWS service recommendations.

Implementation and Follow-Up:

To ensure effective implementation of recommendations:

• Advisory Plan: Offers quarterly follow-ups to review progress and make necessary adjustments, focusing on AWS service utilization and optimization.

• Project Team Deployment: Deploys a project team dedicated to developing the recommended changes within the proposed timeframe, utilizing AWS services for efficient execution.

Report Content and Remediation Considerations

• Report Body: Provides detailed evidence of identified risks across team dynamics, technical processes, tech stack, cloud infrastructure, and architecture, with a focus on AWS services and best practices.

• Well-Architected Solutions Architecture: Includes architectural diagrams and documentation outlining technology solutions to address identified challenges and achieve strategic objectives using AWS services. These services include Amazon SageMaker, Amazon Rekognition, Amazon Bedrock and other AI-based services provided by AWS.

• Implementation Roadmap: Details a roadmap for implementing recommended architectural and technical changes, complete with a work-breakdown structure, and integrates AWS services for streamlined execution.

• Blueprint for Operational Excellence: Provides a blueprint for integrating effective DataOps, DevSecOps, and MLOps processes for your machine learning and AI workloads using AWS services.