MLOps.community

Guest Catherine Nelson, author of 'Software Engineering for Data Scientists', discusses the importance of data scientists learning software engineering principles. Topics include transitioning to production-ready code, roles in data science, challenges in model evaluation, and the continuous learning journey in data science.

Delve into Meta's innovative AI infrastructure, handling trillions of model executions daily. Explore training challenges, GenAI optimizations, Roce networking, and Meta's commitment to open-source AGI. Learn about GPU performance, Linux file systems, and the Ops planner work orchestrator. Discover Meta's transition to advanced AI workloads, cluster maintenance, and performance optimization strategies.

Shaun Wei, CEO of RealChar, discusses the evolution of Rivia, an AI assistant for handling phone calls. Topics include leveraging Generative AI, technical challenges in AI model deployment, and the complexities of self-driving cars. The podcast explores the use of AI to streamline customer service interactions and prioritize more important tasks in daily life.

Richard Howes, a passionate engineer specializing in control systems, discusses balancing safety and technology advancement in heavy industrial settings. The podcast explores utilizing AI and ML for quality assurance, the role of business stakeholders in implementing AI systems, the importance of clear diagrams and SOPs, integrating external data sources for analysis, and challenges faced by data engineers in data management.

Ethan Rosenthal from Runway discusses challenges of multimodal AI, transitioning from language to video models, and tools for content creators. He explores managing large datasets, transitioning research to production, and collaboration with cloud infrastructure tools. Accelerating research and utilizing resources efficiently at startups are also covered.

Boris Selitser, Co-Founder of Okareo, discusses the power of synthetic data and agent evaluations in LLM development. Topics include safeguarding AI products, online evaluation in agent systems, custom evaluations, and the role of synthetic data in AI development. The conversation also explores agent architectures, challenges faced by startups, and the potential of agent frameworks in the tech industry.

Discussion on challenges in deploying AI models in manufacturing, optimizing models for corner cases, transitioning models to production, and exploring trust in traditional ML vs LLMs. Special guests share insights on managing battery health, semiconductor manufacturing, and maintaining reliability and customer trust in industrial settings.

Miguel Fierro, Principal Data Science Manager at Microsoft, discusses the challenges of applying ML in robotics and the integration of computer vision in sports analytics. He highlights the role of AI in strategic game analysis and explores the evolution of recommendation systems, emphasizing the importance of real-time architectures for personalized recommendations.

Demetrios Brinkmann, AI strategist at Uber, discusses the evolution of Michelangelo platform at Uber, from basic ML predictions to deep learning and generative AI. Covering challenges faced in early versions and improvements in Michelangelo 2.0 and 3.0 like Pytorch support, enhanced model training, and integration of technologies like Nvidia’s Triton and Kubernetes. The platform now includes features like a Genai gateway, compliance guardrails, and model performance monitoring to streamline AI operations.

Join Kamran Khan and Matthew McClean as they discuss AWS Trainium and Inferentia, powerful AI accelerators offering enhanced performance and cost savings. They delve into integration with PyTorch, JAX, and Hugging Face, along with support from industry leaders like W&B. Explore the evolution and performance comparison of these AI chips, flexibility in model training with Trainium, and workflow integration with SageMaker. Discover the distinctions between inference and training on accelerators and explore AWS services for generative AI.