Chain of Thought

This week, a panel of experts (Mehmet Murat Ezbiderli, ServiceTitan; Grant Ledford, Indeed; and Vinnie Giarrusso, Twilio) join Atin Sanyal (CTO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) to explore the challenges and opportunities of deploying GenAI at enterprise scale in a conversation that's a wake-up call for any business leader looking to harness the power of AI. Together, Atin & Conor break down key considerations like performance, cost, and model selection, emphasizing the need for robust evaluation frameworks and a shift in developer mindset. Atin then sits down with our panel of AI engineering experts to discuss their firsthand experiences with enterprise AI, including the trade-offs of building AI systems, the evolving tools and frameworks available, and the impact these technologies are having on their organizations. Chapters: 01:27 Enterprise Scale Deployment 05:17 Cost, Performance, and Model Selection 08:59 Building and Integrating GenAI Systems 15:26 Emerging Enterprise Use Cases 18:12 Predictions for AI in 2025 27:28 Panel Discussion: Deploying AI at Enterprise Scale 31:19 Gen AI Solutions and Challenges 33:12 Building & Deploying Traditional Infrastructure vs GenAI Infrastructure 34:36 How to Assemble Your GenAI Stack 40:39 Today's Best GenAI Use Cases 48:15 Enterprise AI Trends for 2025 50:36 Closing Remarks and Future Outlook Show Notes: ⁠⁠⁠⁠⁠Watch Productionize 2.0⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Check out Galileo⁠⁠⁠⁠⁠ Follow⁠ ⁠⁠⁠ ⁠⁠⁠Atin Sanyal Follow⁠ ⁠⁠⁠ ⁠⁠⁠⁠Mehmet Murat Ezbiderli Follow ⁠Grant Ledford Follow Vinnie Giarrusso

As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential.  Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thought we’re joined by Chip Huyen (Storyteller, Tép Studio), Vivienne Zhang (Senior Product Manager, Generative AI Software, Nvidia) for a discussion on AI evaluation best practices.  Before we hear from our guests, Vikram Chatterji (CEO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) give their takes on the complexities of AI evals and how to overcome them through the use of objective criteria in evaluating open-ended tasks, the role of hallucinations in AI models, and the importance of human-in-the-loop systems. Afterwards, Chip and Vivienne sit down with Atin Sanyal (Co-Founder & CTO, Galileo) to explore common evaluation approaches, best practices for building frameworks, and implementation lessons. They also discuss the nuances of evaluating AI coding assistants and agentic systems. Chapters: 00:00 Challenges in Evaluating Generative AI 05:45 Evaluating AI Agents 13:08 Are Hallucinations Bad? 17:12 Human in the Loop Systems 20:49 Panel discussion begins 22:57 Challenges in Evaluating Intelligent Systems 24:37 User Feedback and Iterative Improvement 26:47 Post-Deployment Evaluations and Common Mistakes 28:52 Hallucinations in AI: Definitions and Challenges 34:17 Evaluating AI Coding Assistants 38:15 Agentic Systems: Use Cases and Evaluations 43:00 Trends in AI Models and Hardware 45:42 Future of AI in Enterprises 47:16 Conclusion and Final Thoughts Show Notes: ⁠⁠⁠⁠Watch Productionize 2.0⁠⁠⁠⁠ ⁠⁠⁠Check out Galileo⁠⁠⁠⁠ Follow⁠ ⁠⁠⁠ ⁠⁠Vikram Chatterji⁠ Follow⁠ ⁠⁠⁠ ⁠Chip Huyen Follow Vivienne Zhang

The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems. Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment?” This week on Chain of Thought, Galileo’s CEO, Vikram Chatterji joins Conor Bronsdon to discuss AI's value proposition, from the initial hype to the current search for tangible returns, offering insights into how businesses can identify the right AI use cases to maximize their investment. Next, we’re joined by a panel of AI experts to discuss the ROI of Enterprise AI, featuring Alex Klug, Head of Product, Data Science & AI at HP; Sriram Palapudi, Sr. Dir, ML Platform Engineering at ServiceNow; and Jay Subrahmonia, Global MD for AI Research & Products at Accenture. Together, they explore effective implementation strategies, how to measure the returns of AI adoption in the enterprise, and why AI's ROI isn't always just about the bottom line. Chapters: 00:00 Introduction 01:50 Current State of AI Investments 03:59 Challenges and Solutions in AI Implementation 08:30 Identifying and Prioritizing AI Use Cases 10:53 Ensuring Trust and Explainability in AI 15:29 Measuring ROI and Efficiency Gains 21:10 Panel Discussion Begins 21:54 Trust and Risk Management at HP 23:27 Accenture's Approach to Operationalizing AI 26:06 ServiceNow's Trade-offs and Prioritization 31:17 Measuring the success of AI for customers 36:29 Frameworks and Best Practices 40:57 Conclusion and Final Thoughts Show Notes: ⁠⁠⁠Watch Productionize 2.0⁠⁠⁠ ⁠⁠Check out Galileo⁠⁠⁠ Follow⁠ ⁠⁠⁠ ⁠Vikram Chatterji Follow⁠ ⁠⁠⁠ Alex Klug Follow Sriram Palapudi Follow Jay Subrahmonia

Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge? This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025. Listen as Sara Hooker, VP of Research at Cohere and Head of Cohere for AI predicts a trend towards smaller, more optimized AI models; Craig Wiley, Senior Director of Product, Mosaic AI at Databricks, dives into the future of multimodal AI; and Galileo’s CEO, Vikram Chatterji, shares his predictions, including the rise of open-source LLMs. Chapters: 00:00 Introduction 02:01 Vikram's top 3 predictions 06:19 AI and nuclear energy 08:30 Giving power back to the people 13:46 Craig's predictions 20:46 The "era of toolification" 30:38 Sara's predictions 35:07 AI safety Show Notes: ⁠⁠Watch Productionize 2.0⁠⁠ ⁠Check out Galileo⁠⁠ Follow⁠ Sara Hooker Follow⁠ ⁠⁠⁠Craig Wiley Follow⁠ ⁠⁠⁠ Vikram Chatterji