Harnessing the Power of Custom AI: Maximizing ROI Through Tailored Model Optimization

Key Insights from the AWS Generative AI Innovation Center’s Custom Model Program

Exploring AWS’s Custom Model Program

In 2024, we proudly launched the Custom Model Program as part of the AWS Generative AI Innovation Center. This initiative is designed to provide extensive support for organizations seeking to customize and optimize their AI models. Over the past two years, we’ve partnered with a diverse array of enterprises and startups in industries ranging from legal and financial services to healthcare, software development, telecommunications, and manufacturing. These collaborations have resulted in AI solutions tailored to fit each organization’s unique data, brand voice, and business needs, proving to be more effective and cost-efficient than traditional off-the-shelf options.

As businesses progress beyond basic proof-of-concept projects and chatbots, we are witnessing a surge in the adoption of more advanced personalization and optimization methods. Our approach includes creating specialized models for specific tasks, refining larger models into smaller, faster versions, and enhancing hardware capabilities to optimize performance while driving down costs.

The Power of Strategic Investments

Strategic upfront investments in AI customization have shown remarkable returns, as demonstrated by Cosine AI, which developed an AI assistant integrated into users’ workflows. By working closely with the Innovation Center, they refined their Amazon Nova foundation model, resulting in a dramatic increase in A/B testing capability, faster development iterations, and overall project speed improvements. This example highlights how transitioning toward agentic systems—where speed, performance, and task precision are paramount—can yield significant rewards.

In this post, we’ll discuss five key strategies for maximizing ROI when customizing AI models, as well as pitfalls to avoid.

Five Tips for Maximizing Value from Training and Tuning Generative AI Models

1. Start with Business Goals

It may seem obvious, but the key to a successful AI project lies in starting with clear business objectives. By focusing on measurable outcomes, we ensure that technical decisions align with strategic goals. Customers often approach us with specific technical solutions in mind without having defined use cases or evaluation plans. By working backwards from business aims, we ensure that projects yield real, measurable value.

2. Choose the Right Customization Approach

Instead of jumping straight into deep customization, it’s essential to start with a baseline approach, exhausting simpler options first. Techniques such as prompt engineering and retrieval-augmented generation (RAG) often provide effective solutions without the need for deep intervention. For example:

• Supervised Fine-Tuning: Sharpens models for specific uses, as seen with Volkswagen, which enhanced brand consistency checks.
• Domain-Specific Foundation Models: Organizations like TGS utilize custom AI models to address highly specialized challenges.

By beginning with lighter strategies, organizations can validate their approaches and collect valuable data for future iterations.

3. Define Clear Metrics for Success

Measurable success criteria are vital. Metrics should reflect what genuinely matters to the organization’s goals, rather than relying on generic performance indicators. For instance, Volkswagen improved brand compliance by fine-tuning its models based on marketing expertise, aligning AI output with their brand identity. Automated evaluation processes also support iterative improvements.

4. Optimize at the Hardware Level

Using managed services like Amazon Bedrock offers built-in optimizations. However, organizations operating at lower levels can achieve significant efficiency gains by optimizing hardware infrastructure. For instance, TGS realized nearly linear scaling in processing seismic data by upgrading to AWS’s high-performance GPU infrastructure, allowing them to deliver insights faster than ever.

5. Embrace a Modular Approach

There’s no one-size-fits-all model. While larger models may excel in certain general tasks, they often come with higher computational costs. Adopting a modular architecture allows organizations to utilize various models tailored to specific tasks, optimizing for cost and performance. Services like Amazon Bedrock facilitate this through unified API access to diverse model families, promoting flexibility and rapid adaptation to innovations in AI.

How the Innovation Center Can Assist

The Custom Model Program at the Innovation Center provides expert support at every stage, from model selection to customization and integration. Our dedicated teams embed with customers to ensure that solutions remain aligned with their business strategies and objectives. This end-to-end support has enabled organizations across various sectors to transform visionary AI initiatives into tangible business results.

Want to Learn More?

Interested in optimizing your AI capabilities? Connect with your account manager or explore us at AWS re:Invent.

About the Authors

Sri Elaprolu leads the AWS Generative AI Innovation Center, drawing on nearly 30 years of technological leadership to deliver cutting-edge AI solutions.

Hannah Marlowe oversees the Model Customization and Optimization program, steering specialized teams toward tailored AI solutions.

Rohit Thekkanal manages ML engineering for model customization, ensuring scalable and impactful implementations.

Alexandra Fedorova leads growth initiatives for the program, driving strategic partnerships and expansions.

By leveraging the expertise and resources at the AWS Generative AI Innovation Center, businesses can navigate the complexities of AI customization to unlock its full potential—creating impactful, cost-effective solutions tailored to their unique needs.