Deploying Generative AI at Enterprise Scale: Navigating Challenges and Unlocking Potential

Deploying Generative AI (GenAI) at an enterprise scale presents both immense opportunities and significant challenges. On the "Chain of Thought" podcast, Galileo's Co-founder and CTO, Atindriyo Sanyal, and Head of Developer Awareness, Conor Bronsdon, delve into key considerations for successfully scaling GenAI in businesses.

They break down essential aspects like performance optimization, cost management, model selection, and the importance of robust evaluation frameworks, emphasizing the need for a shift in developer mindset to fully harness AI's potential.

The Importance of a Rigorous Evaluation Framework

Deploying GenAI at scale requires the right tools and accurate measurement. Traditional metrics worked for basic language models but fall short with large language models (LLMs). These older metrics can't keep up with the dynamic data GenAI handles in real life.

"These applications falter in real-world scenarios," Sanyal points out, because data changes and old evaluation methods can't keep up.

Businesses need a solid and flexible evaluation framework. There's no one-size-fits-all when it comes to performance evaluation.

Galileo's approach offers customizable evaluation criteria that adapt to specific business needs. Implementing robust evaluation methods helps businesses tailor evaluation criteria to their needs.

A strong evaluation framework impacts both performance and cost management. Tailored metrics, such as critical thinking benchmarks and appropriate metrics for AI agents, help streamline AI workflows, aligning them with business goals without wasting resources. Identifying key performance metrics for AI is essential in developing such a framework.

Robust evaluation frameworks ensure AI systems are reliable, perform well, and stay secure and compliant. With a thorough evaluation approach, businesses can unlock AI's full potential, deploying it sustainably and securely.

Integrating GenAI into Enterprise Systems

Adding GenAI to existing systems is a priority for businesses wanting to harness AI but isn't without challenges. It takes careful planning to blend new capabilities with current workflows.

One major issue is the maturity of GenAI tools. Sanyal notes a discrepancy around performance, especially when moving from prototypes to real-world use. Legacy systems often clash with evolving GenAI solutions because they weren't built for the dynamic data these new systems handle.

Cost is another critical factor.

These systems are not cheap, and scaling AI can lead to runaway expenses, especially with millions of daily queries. To keep costs in check, businesses need innovative querying patterns and to rethink their evaluation strategies.

This might mean optimizing model choices, deciding between cloud and on-premise solutions, or exploring open-source options to balance costs and performance.

To address these challenges, leveraging synthetic data for AI can aid in testing and refining AI models without interfering with live systems. Additionally, building AI models with quality data is essential to ensure compatibility and performance when integrating GenAI into enterprise systems.

Successful GenAI integration requires a gradual, step-by-step approach:

• Incremental Integration: Slowly introduce GenAI elements to existing systems to minimize risks.
• Collaborative Teams: Combine familiar tools with new AI technologies, encouraging collaboration across teams.
• Robust Experimentation: Focus on experimentation and real-time monitoring to fine-tune systems.

Ignoring the nuances of GenAI integration can lead to unchecked costs and mismatched workflows. It's crucial to have a solid observation framework and make data-driven decisions. Following best practices paves the way for smoother operations and innovative capabilities.

Cost Management and Model Selection

Deploying GenAI at scale means juggling cost, performance, and model choices. Companies often face the decision: use LLM APIs from providers like OpenAI or build and host models in-house.

Each path has trade-offs affecting flexibility, upfront costs, and scalability. Understanding different strategies for optimizing LLM performance is crucial in making these decisions.

Choosing Between LLM APIs and On-Premises Hosting

Using LLM APIs allows quick deployment without heavy infrastructure investments. However, performance can be unpredictable in real-world scenarios. Costs can climb quickly as usage increases. Bronsdon warns, "It's really easy also to see this ballooning cost effect." Hosting models on-premises requires a higher initial investment but can lower long-term costs if managed correctly.

Evaluating Training and Inference Costs

Understanding the difference between training and inference is key. Fine-tuning models can be cheaper than constant prompting.

Inference costs can be reduced by choosing models that fit specific needs. Open-source models, for example, can be more cost-effective for inference, especially when custom features are needed.

Choosing Between Open-Source and Proprietary Models

Selecting between open-source and proprietary models involves weighing license costs, community support, and adaptability. The quality of open-source models is "extremely high" nowadays.

Open-source solutions let companies tweak and adapt models for specific applications without restrictive licensing fees. While proprietary models might offer better performance, open-source models can perform comparably.

Guidelines for Decision-Makers

• Assess Model Fit: Evaluate business needs and select models accordingly. Scale usage to gain cost benefits from on-prem hosting.
• Invest in Fine-Tuning: Fine-tuning can lower training and inference costs and better align models with business goals.
• Explore Open-Source Options: Open-source models offer flexibility and savings, allowing for custom tweaks.
• Monitor and Adjust: Use a solid evaluation framework to monitor performance and costs, adjusting as needed.

Balancing these elements helps businesses use GenAI wisely, ensuring tech investments match strategic goals while keeping costs under control.

Security and Compliance Challenges

Integrating GenAI into enterprise systems requires addressing performance and cost issues, as well as tackling security and compliance challenges head-on. As companies dive deeper into AI, they face a complex mix of data protection, privacy, and regulatory requirements.

Moving from simple AI demos to full-scale GenAI deployments brings a host of challenges. In sensitive areas like healthcare and finance, the stakes are high—a breach or compliance failure can have serious consequences. Ensuring AI regulation compliance is essential to navigate these complexities.

Galileo steps in with its Protect module, designed to shield AI systems from risks like data leaks and biases. The Protect module helps businesses secure their AI workflows, ensuring deployments are safe and meet industry standards.

To meet these security demands, companies need comprehensive strategies to bolster their AI systems’ resilience. Rigorous model evaluation, thorough testing, and ongoing monitoring are crucial steps to spot and mitigate risks. Adopting a privacy-first approach and investing in top-notch encryption technologies also play a significant role in strengthening AI security.

As data grows more dynamic and harder to protect, Galileo’s innovative metrics provide clear insights into AI performance and potential weaknesses. Such insights are essential for taking proactive measures, helping businesses stay compliant and secure.

Handling security and compliance in GenAI presents both technical and strategic hurdles. Businesses need to keep up with changing regulations and use adaptable AI solutions like Galileo’s Protect module to safeguard their data while reaping the benefits of AI advancements.

Future Outlook for GenAI in Enterprises

The future of GenAI in enterprises is promising, with advancements set to transform how AI integrates into business operations. One area of growth is AI reasoning abilities. Sanyal predicts that by 2025, models will have much more sophisticated reasoning power, enabling them to handle complex tasks independently.

These advancements will open up new possibilities for AI applications, making decision-making smarter and more efficient.

Cost reduction is another trend on the horizon. The industry has seen "a massive reduction in the cost of intelligence," with smaller models performing as well as larger ones. This cost reduction will likely continue, making advanced AI more accessible.

Lower costs mean companies can deploy AI solutions at scale without prohibitive expenses.

We'll also see more use of agents and multimodal systems in enterprise settings. Currently, these systems are still emerging but will mature and evolve, moving from basic chatbots to sophisticated problem-solvers that handle diverse data inputs and outputs. These advancements will enable more nuanced and integrated AI interactions within existing workflows.

As GenAI matures, its use cases in enterprises will expand, moving beyond customer service and content generation into areas like legal affairs and human resources. "We saw certain use cases emerge as some of the obvious ones to go to while we saw a lot of expansion," says Sanyal, highlighting the potential for AI to touch every part of a business.

Successful adoption of GenAI depends on ongoing innovation. Companies need to balance using tools their teams already know with embracing new AI technologies. You should have a more cautious approach to baking newer capabilities in your legacy systems along with building robust experimentation frameworks.

Staying Ahead in GenAI Development

Staying ahead in GenAI means keeping up with innovations and effectively integrating new technologies. Companies like Galileo, which lead with strong evaluation, observability, and protection features, show that robust AI systems require solid foundations.

For more insights on GenAI deployment, listen to the full episode where a panel of experts including Sanyal, Mehmet Murat Ezbiderli from ServiceTitan, Grant Ledford from Indeed and Vinnie Giarrusso from Twilio, discuss deployment and scaling challenges.

Learn more about how Galileo can help you secure your GenAI applications.