Generative artificial intelligence (AI) is reshaping businesses, industries, markets, and economies at a rapid rate. Moving from GenAI adoption to successful deployments is not just a matter of remaining competitive, but of thriving in a fast-evolving business environment. With so much on the line, it’s smart to focus on the four key parts of a successful GenAI deployment.
Part 1: Model Selection
From proprietary to open-source options, GenAI is available in many different models, versions, and applications. Each has its strengths and weaknesses, but many may not be readily apparent. Additionally, selecting a model to fit specific tasks is vital.
Further, you’ll need to check the availability and quality of the model’s training data, computational requirements, app development and integration options, and ongoing support requirements. The availability of skilled AI talent will affect how much of that you can do internally, and how much you may need third-party help with.
Customizing a model to further match your organization’s goals and requirements is another important way to get the performance your company needs from GenAI. Understanding and accessing customization capabilities against task requirements and complexity are key. For example, fine-tuning a model is a more complex task than customizing with retrieval augmented generation (RAG) and/or system messages.
Other factors can also come into play such as usage caps, token costs, and potential availability throttling by a vendor.
Part 2: Architecture Solution
The best GenAI model for your organization will still fall short of expectations or fail altogether if it isn’t properly supported by the right architecture. It is important to fully assess AI readiness before your organization deploys a model, and to maintain the architecture to meet increasing GenAI use in your organization.
In many cases, it is simpler and more efficient to leverage a cloud provider’s architecture for GenAI support. AWS architecture is built to suit all forms of AI models from machine learning to GenAI ensuring both a strong environment for AI use and broad versatility in AI model deployments. Coupling these advantages with a GenAI platform built to run natively on AWS further ensures smoother deployments and a higher probability of success.
Part 3: Prototype Development
Prototypes are important to testing and evaluating specific parts and performance of systems. The results of these evaluations can then be used to further develop and refine the accuracy of AI models.
It’s often a complex process that can be made much simpler with the right tools, strong guidance, and a bit of experience.
Part 4: Implementation Roadmap
According to a World Economic Forum report, “Having robust frameworks in place will provide a roadmap, guiding companies through the complex landscape of GenAI implementation and promote trust.”
This part requires strategic thinking and problem-solving as well a firm understanding of governance, compliance, data privacy, and security issues.
Where there are available tools to assist you with each of these parts, a single solution, like The AWS & Virtusa Generative AI and Data Lab, can provide significant benefits to help your business achieve a fully optimized architecture and roadmap.
The Generative AI and Data Lab provides a variety of offerings, including expediting initial data and AI/ML launches to AWS and providing a customized approach to scale competencies and processes for the future on AWS. AWS and Virtusa’s multi-disciplinary experts offer a holistic approach to AI, ML, and cloud-based data. They guide businesses through various stages, which include formulating business strategies, creating migration and ML operational roadmaps, optimizing cloud migration, and establishing a modernized platform for continuous data and ML operations.
Virtusa and AWS empower businesses to unlock the transformative power of data, which can fuel innovation and elevate end-customer experience with AWS-specific solutions and Virtusa accelerators.
To explore how Generative AI and Data Lab and other Virtusa offerings can help optimize your Gen AI deployment, visit virtusa.com.