Operational Excellence for Generative AI Workloads
Large enterprises are building strategies to harness the power of generative artificial intelligence (AI) across their organizations. However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level.
The AWS Well-Architected Framework
The AWS Well-Architected Framework was developed to allow organizations to address the challenges of using Cloud in a large organization, leveraging the best practices and guides developed by AWS across thousands of customer engagements. AI introduces some unique challenges, including managing bias, intellectual property, prompt safety, and data integrity, which are critical considerations when deploying generative AI solutions at scale.
Amazon Bedrock
Amazon Bedrock plays a pivotal role in this endeavor. It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Scalability, Security, and Operational Efficiency
With Amazon Bedrock, enterprises can achieve:
- Scalability – Scale generative AI applications across different LOBs
- Security and compliance – Enforce data privacy, security, and compliance with industry standards and regulations
- Operational efficiency – Streamline operations with built-in tools for monitoring, logging, and automation, aligned with the AWS Well-Architected Framework
- Innovation – Access cutting-edge AI models and continually improve them with real-time data and feedback
What’s Different About Operating Generative AI Workloads?
The operational excellence pillar of the Well-Architected Framework helps your team to focus more of their time on building new features that benefit customers, in our case, the development of GENAI solutions in a safe and scalable manner. However, if we were to apply a generative AI lens, we would need to address the intricate challenges and opportunities arising from its innovative nature.
Policy, Guardrails, and Mechanisms
Any generative AI lens therefore needs to combine the following elements, each with varying levels of prescription and enforcement, to address these challenges and provide the basis for responsible AI usage:
- Policy – The system of principles to guide decisions
- Guardrails – The rules that create boundaries to keep you within the policy
- Mechanisms – The process and tools
Organizing Teams Around Business Outcomes
We start this post by reviewing the foundational operational elements defined by the operational excellence pillar:
- Organize teams around business outcomes: The ability of a team to achieve business outcomes comes from leadership vision, effective operations, and a business-aligned operating model. Leadership should be fully invested and committed to a CloudOps transformation with a suitable cloud operating model that incentivizes teams to operate in the most efficient way and meet business outcomes.
- Implement observability for actionable insights: Gain a comprehensive understanding of workload behavior, performance, reliability, cost, and health. Establish key performance indicators (KPIs) and leverage observability telemetry to make informed decisions and take prompt action when business outcomes are at risk.
- Safely automate where possible: In the cloud, you can apply the same engineering discipline that you use for application code to your entire environment. You can define your entire workload and its operations (applications, infrastructure, configuration, and procedures) as code, and update it. You can then automate your workload’s operations by initiating them.
Managing Data
Managing data through standard methods of data ingestion and use is imperative for LLMs to provide more contextual answers without the need for extensive fine-tuning or the overhead of building a specific corporate LLM. Managing data ingestion, extraction, transformation, cataloging, and governance is a complex, time-consuming process that needs to align with corporate data policies and governance frameworks.
Providing Managed Infrastructure Patterns and Blueprints
There are a number of ways to build and deploy a generative AI solution. AWS offers key services such as Amazon Bedrock, Amazon Kendra, OpenSearch Service, and more, which can be configured to support multiple generative AI use cases, such as text summarization, Retrieval Augmented Generation (RAG), and others.
Conclusion
By focusing on the operational excellence pillar of the Well-Architected Framework from a generative AI lens, enterprises can scale their generative AI initiatives with confidence, building solutions that are secure, cost-effective, and compliant. Introducing a standardized skeleton framework for generative AI runtimes, prompts, and orchestration will empower your organization to seamlessly integrate generative AI capabilities into your existing workflows.
FAQs
Q: What are the challenges of scaling up generative AI?
A: The challenges of scaling up generative AI include ensuring data privacy and security, legal, compliance, and operational complexities are governed on an organizational level.
Q: What is the AWS Well-Architected Framework?
A: The AWS Well-Architected Framework is a set of best practices and guides developed by AWS to help organizations build and run workloads in the cloud.
Q: What is Amazon Bedrock?
A: Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Q: What are the key elements of a generative AI lens?
A: The key elements of a generative AI lens are policy, guardrails, and mechanisms, which are used to address the intricate challenges and opportunities arising from the innovative nature of generative AI.
