Eviden’s GenOps Framework is a robust solution designed to streamline the transition of generative AI projects from pilot to full-scale production, leveraging AWS's comprehensive suite of AI/ML services. With over 15 years of experience in AI/ML deployments, our framework builds on the successful MLOps 10-step methodology, ensuring unmatched scalability, security, and compliance.

The GenOps Framework supports a wide array of generative AI models, including over 50 types of Large Language Models (LLMs). Utilizing AWS services like Amazon Bedrock and Amazon SageMaker, it facilitates efficient model training, deployment, and management, enabling organizations to maximize their AI investments and achieve operational excellence.

The GenOps approach begins with a detailed 150-point checklist to assess your current generative AI capabilities and identify areas for enhancement. Based on this assessment, we design a customized solution that aligns with your specific needs, using our curated and scored product list and reference architectures. This strategy employs a structured 10-step methodology to ensure optimal deployment. Key steps include setting up a sandbox environment, selecting and fine-tuning foundational models, and deploying them into production with robust monitoring and maintenance. Additionally, the GenOps Management Console adds a critical observability layer, offering a unified view to control costs (FinOps), monitor security (SecOps), track performance, and manage AI assets efficiently, leveraging AWS's powerful infrastructure for seamless integration and scalability.

One of the standout features of GenOps is its commitment to responsible AI, with comprehensive governance and security measures built into every stage. Leveraging Guardrails for Amazon Bedrock, the framework provides AI safety across all applications, allowing organizations to implement specific policies for evaluating user inputs and model responses. Guardrails can block undesirable topics, filter harmful content, redact sensitive information, and use custom word filters, ensuring a relevant and safe user experience. This integration with AWS services not only ensures data privacy and ethical AI usage but also empowers organizations to build generative AI applications aligned with their responsible AI policies, driving innovation while maintaining a competitive edge.