An In-Depth Look at How FAIR Accelerates AI Adoption
By Nirmal Ranganathan, Chief Architect – Data & AI, Rackspace Technology
Last month, we announced the spin-up of The Foundry for AI by Rackspace (FAIR™) — our ground-breaking practice dedicated to accelerating the secure, responsible and sustainable adoption of generative AI solutions. This announcement represents yet another significant milestone in our 25-year history of helping companies adopt, manage and optimize emerging technologies.
Generative AI has evolved rapidly, offering tremendous potential to create content, reduce errors, increase productivity and optimize costs through its ability to comprehend natural language and context at previously impossible levels. And the benefits of generative AI extend to jobs in just about every industry:
- Engineering and development teams will accelerate and improve software development and refinement.
- Content creators will create better content, faster.
- Enterprise CIOs and CDOs will better leverage their internal systems and IT infrastructure to unlock valuable insights and bridge the gaps in data availability.
- Process workflows, documentation generation and back-end systems will benefit from automation.
Since we recently shared some CIO perspective on developing a generative AI strategy, I thought this is a good time to provide deeper insight into the engagement model that powers FAIR, and our ability to help you put those workloads into production quickly.
FAIR offers customers significant flexibility in how they adopt generative AI. You can engage with any of our three core services from any stage in the generative AI adoption process, but we generally find that customers want to proceed in the order of Ideate, Incubate and Industrialize. Let’s look at some of the specific processes involved at each stage, remembering that this methodology is designed to help you move quickly and decisively.
Generative AI Ideate
This will be the starting point for most customers as we help you determine your business goals and define a top-priority generative AI use case. We will help you to quickly establish intended outcomes, develop a unique action plan based on your internal capabilities and define your top-priority use case for further development.
Here’s what a typical Generative AI Ideate engagement looks like:
- Explore the possibilities: We look at how generative AI can benefit your organization and drive innovation.
- Assess the impact: We evaluate the intended and unintended consequences of integrating generative AI into your operations to help create a comprehensive understanding of its implications.
- Evaluate data quality: We work closely with your teams to establish the quality and integrity of your data, which is a crucial ingredient for successful generative AI implementation.
- Plan for responsible AI: We help you plan how to embed your company values, fairness and governance into your generative AI operations to help ensure responsible and ethical practices.
Our Generative AI Ideate engagement takes you through three sprints designed to help you establish relevant generative AI capabilities, build an action plan and define a top-priority use case.
- Sprint 1: Determine the baseline
This sprint focuses on developing your first use case and building the necessary capabilities to successfully adopt generative AI within your organization. We help you establish a clear understanding of the benefits that generative AI can bring to your organization and recommend ways to bridge any operational and organizational gaps.
- Sprint 2: Develop an action plan
Through immersive and collaborative working sessions, we transform your desired business outcomes into a concrete action plan. This sprint is designed to collectively assess your current capabilities, organizational capacities and immediate priorities while identifying the highest-potential use cases that can demonstrate the feasibility of generative AI.
- Sprint 3: Define a top-priority use case
Together, we identify a top-priority use case for generative AI that aligns with your goals. This stage involves developing the first use case and enhancing the capabilities needed to successfully adopt generative AI within your organization. With these deliverables, you gain a clear roadmap for embracing generative AI and driving transformative outcomes.
Generative AI Incubate
The next stage of generative AI development and implementation transitions from planning to the co-creation of your first generative AI solution within your enterprise. Our Generative AI Incubate engagement adopts an agile, iterative and time-boxed approach to demonstrate the feasibility of building an AI solution within your organization.
Here are some of the ways FAIR can help build your minimum viable product (MVP):
- Selecting an existing foundation model: We will choose a reliable base model as a starting point for your AI solution.
- Defining and setting up the cloud platform: Our experts will help you determine the ideal cloud platform and configure it for your specific needs.
- Preparing data, adapting and aligning the model: We will handle the data preparation to help ensure that it aligns with the AI model's requirements.
- Fine-tuning and prompt engineering of LLMs: Our team will refine and integrate the large language models (LLMs) to optimize model output.
- Demonstrating, optimizing and augmenting the model: We will showcase your AI solution, optimize it based on feedback and explore opportunities for further enhancements.
- Building an LLM-powered application: Finally, we will create an application that leverages the power of large language models.
Generative AI Incubate takes you through a series of iterations. Each iteration brings us closer to a refined and high-performing AI solution tailored to your organization's unique needs. Let's look at that journey:
- Iteration 1: Discovery and Design
During this initial iteration, we delve into the requirements for your MVP. We work with you to define data sources, establish the AI platform and outline the technology architecture. Key deliverables include the identification of a suitable Large Language Model (LLM), determining the fine-tuning approach, and creating prompt templates and definitions.
- Iterations 2-3: Adapt and Align
In these iterations, we select a foundational model and begin the process of adapting, aligning and fine-tuning it to achieve a baseline performance for a specific task. We establish data pipelines and implement Machine Learning Operations (MLOps) for efficient data flow. The outcome includes an MVP that is tuned for the targeted task, reparameterization of the model to align it with your requirements, and prompt tuning to optimize its performance.
- Iterations 4-6: Optimize and Augment
Here, the focus shifts to optimization and augmentation of your AI solution. Through prompt engineering and meticulous training, we fine-tune the model's performance against specific tasks. We adjust parameters such as prompts, temperature, and P and K values to achieve the desired results. We also develop a user interface that enables seamless interaction with the inference. Throughout these iterations, we identify any remaining gaps that need to be addressed before scaling and industrializing the AI solution.
Generative AI Industrialize
The third engagement phase focuses on making generative AI a successful and sustainable part of your organization. FAIR follows a systematic and planned approach ensuring the viability of generative AI by establishing:
- A robust framework: We incorporate DataOps, MLOps and LLMOps to help ensure smooth data delivery, data management, efficient machine learning operations and effective utilization of large language models.
- Governance and guardrails: To help you maintain control and mitigate risks, it's essential to define policies, procedures and guidelines that will govern the use and ethical considerations of generative AI. With these guardrails, you can promote responsible and accountable AI usage within your organization.
- End-to-end layered security: Refine security of the data and AI models to handle appropriate data classifications, authorization, model integrity, privacy and compliance, intellectual property safeguards and infrastructure and network layer security controls.
- Processes for continuous optimization: To measure the effectiveness of generative AI and drive continuous improvement, you must define relevant metrics and establish a process for optimization. By tracking performance indicators and leveraging insights, you can refine the AI model for sustainability and cost-effectiveness.
Take the next steps
In closing, we are excited to embark on this journey of generative AI adoption with you. As we move forward, we will remain dedicated to the principles of responsible AI. Our mission is to ensure that generative AI is accessible to everyone, and that it is applied in a manner that benefits us all. We firmly believe that AI has the power to enhance our work and help us perform our jobs better, and we are committed to leveraging its potential responsibly and ethically.
And this is just the beginning. As hyperscale public cloud providers continue to introduce new AI capabilities, we’ll be here to help you adopt and utilize them fully.
Our generative AI services are part of the Foundry for AI by Rackspace Technology.
Foundry for AI by Rackspace (FAIR™) is a groundbreaking global practice dedicated to accelerating the secure, responsible, and sustainable adoption of generative AI solutions across industries. Follow FAIR on LinkedIn.
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