MLOps.community

Kai Wang, product lead of Uber's AI platform team, dives into the fascinating world of Uber's Michelangelo, the internal ML platform that powers critical business use cases. He discusses how Uber's approach to AI goes beyond buzzwords, focusing on tools for both novice and advanced users. Kai sheds light on predictive and generative models, detailing how they enhance services like ride booking and Uber Eats. He also hints at exciting plans to open-source parts of their framework, underscoring the importance of community collaboration in AI innovation.

Nikolaus West, CEO of Rerun and a Stanford computer vision alum, dives into the intriguing world of physical AI and robotics. He discusses how traditional software struggles in dynamic environments and emphasizes the need for better visualization and adaptability tools. West explores how physical AI could revolutionize industries, contrasting it with the stagnation of IoT. He also highlights the complexities of training AI, the challenges of data management in robotics, and the importance of balancing sensor types and modeling approaches in creating effective solutions.

Kostas Pardalis and Yoni Michael, co-founders of Typedef, dive into the transformative power of AI and data in today’s business landscape. They discuss how LLMs are revolutionizing data pipelines and the vital role of AI Reliability Engineers in ensuring system stability. The duo emphasizes the need for robust infrastructure and collaboration between engineers and domain experts to tackle integration challenges. They also explore the evolving dynamics of AI in customer support and the importance of community in driving innovation in technology.

Greg Kamradt, a mentor for AI-centric developers and an expert in launching tech products, dives into the nuances of AI benchmarking. He discusses the challenges in creating effective benchmarks, highlighting the intriguing concept of puzzles that are easy for humans but hard for AI. The conversation covers compute tradeoffs and the philosophical implications of tracking AI progress towards AGI. Greg also shares insights on motivating participants in AI competitions and the evolving ARC framework for assessing intelligence in innovative ways.

Deepti Srivastava, Founder and CEO of Snow Leopard AI, with nearly 20 years in data platforms, shares insights on making AI work for businesses. She discusses the challenges of integrating operational data with AI, emphasizing the need for effective connections to structured data. Deepti advocates for simplifying data pipeline management to enhance real-time analytics. She also highlights innovative tools like Snow Leopard that unlock data accessibility, illustrating the importance of embedding AI into a company’s tech stack for improved decision-making.

Sebastián Ramírez, known as Tiangolo, is the creator of FastAPI and currently leads FastAPI Labs. He dives into his journey launching FastAPI Cloud and shares humorous tales from a recent hackathon. Sebastián discusses the transformative role of Pydantic in streamlining APIs and reflects on FastAPI's impressive growth and adoption, even by NASA. He addresses challenges in software design and the balance of open-source sustainability, emphasizing a user-centric philosophy that enhances the developer experience.

Join Willem Pienaar, CTO of Cleric and creator of Feast, along with PhD student Shreya Shankar, as they tackle the toughest challenges in building AI agents. They discuss the ambiguity of 'ground truth' in evaluations, revealing three key gulfs of human-AI interaction that hinder success. The duo emphasizes the importance of moving humans out of the feedback loop, using implicit signals for faster learning. Practical techniques like heat maps for task failures and the complexities of simulated environments are also explored, shedding light on the inevitable performance ceiling of AI.

In a captivating discussion, Prithviraj Ammanabrolu, an Assistant Professor at UC San Diego and Research Scientist at Databricks, dives deep into the innovative Tao fine-tuning method. This technique allows for training models without labeled data, using reinforcement learning and synthetic inputs. The conversation explores how Tao can enhance small models, optimize limited datasets, and fine-tune outputs effectively. Prithviraj highlights strategies to balance performance, adaptability, and efficiency in machine learning, positioning these advancements as game-changers for model training.

Jukka Remes, a Senior Lecturer and AI Architect, shares insights from his extensive experience in MLOps and AI enablement. He discusses the creation of an open-source MLOps platform designed for flexibility across environments, emphasizing the importance of user-friendly tools for both engineers and non-engineers. Jukka also addresses the challenges of transitioning models from research to production, highlights the need for compliance with evolving regulations, and advocates for collaboration to bridge gaps between technical teams and stakeholders.

Mike Del Balso, CEO and co-founder of Tecton, shares his decade-long journey in AI innovation, including the creation of Uber's Michelangelo ML platform. He discusses the evolution of predictive machine learning use cases, emphasizing the significance of feature stores. Del Balso explores the challenges of real-time data utilization, fraud detection, and the importance of model maturity in business impact. He also highlights the relevance of integrating generative AI with ML for enhanced marketing efficiency, turning complex data into smarter decisions.