AWS Marketplace: FLUID ML – A well-designed review of machine learning workloads on AWS

Our FLUID assessment methodology offers a strategic and comprehensive approach to improving your data and machine learning workloads, ensuring precise alignment with your business goals. 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 start.

By emphasizing iterative improvement, FLUID maintains a continuous project pace, ensuring that no constraints impede sustained progress.

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

Delivery mechanism:

The result of the FLUID assessment is a comprehensive package of results that includes:

• Generated report in PDF format: Powered by our proprietary FLUID assessment tool, this report includes a business-specific questionnaire, combined with our expert analysis using AWS AI and machine learning services.

• Executive summary: A concise overview summarizing the company’s current technology landscape, highlighting strengths, weaknesses, and critical areas for improvement, with detailed recommendations for optimizing AWS services.

• Presentation Guide: Detailed presentation of assessment findings to business and/or technology teams, improving understanding and informing decision-making with insights into AWS services.

• Traffic light analysis: A visual summary that uses a traffic light system to indicate the status and risk level of various components in the technology landscape, with specific recommendations for AWS services.

Implementation and follow-up:

To ensure effective implementation of the recommendations:

• Consulting plan: Offers quarterly check-ins to review progress and make necessary adjustments, focusing on the use and optimization of AWS services.

• Implementation of the project team: Implements a project team responsible for developing recommended changes within the proposed deadline, using AWS services for efficient implementation.

Report content and remedial notes

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

• Well-designed solution architecture: Provides architectural diagrams and documentation that demonstrates technology solutions to address identified challenges and achieve strategic goals using AWS services. These services include Amazon SageMaker, Amazon Rekognition, Amazon Bedrock, and other AI-based services provided by AWS.

• Implementation plan: It provides a detailed implementation plan for recommended architectural and technical changes, along with a work breakdown structure, and integrates AWS services to streamline implementation.

• Operational Excellence Plan: Provides a roadmap for integrating effective DataOps, DevSecOps, and MLOps processes for machine learning and artificial intelligence workloads using AWS services.