MLOps.community

Benjamin Wilms, a chaos and resilience engineering expert, discusses integrating Chaos Engineering into the CI/CD pipeline for system resilience. They explore the cultural shift needed to embrace failures as learning opportunities and the transition to structured chaos engineering experiments. The conversation also covers reflection on errors, AWS's Trainium, chaos engineering methods, and the intersection of chaos engineering and observability for reliable systems.

Tom Smoker, Cofounder of WhyHow.ai, discusses using knowledge graphs in multi-agent systems. Topics include mitigating hallucination issues, optimizing search with knowledge graphs, agile problem-solving, and integrating vector databases. The conversation explores agents in multi-agent systems, stepping back for growth, and using AI models for automated content creation and revenue generation.

Dave Nunez, Partner at Abstract Group, discusses how AI is changing developer documentation strategies. He emphasizes the need to rewrite the developer education playbook for AI-focused software. Topics include enhancing user experience with design cues, escape patches, and intuitive documentation. The importance of clear, user-friendly content and effective onboarding experiences is highlighted.

Cody Peterson, Senior Technical Product Manager at Voltron Data, discusses the importance of open standards in MLOps. Topics include challenges with scalability in data tools like Pandas, leveraging the Ibis project for big data processing, and the power of Apache Arrow in data systems. The conversation also covers transitioning between platforms, considerations for data system selection, and future plans for the Ibis project.

Syed Asad, an Innovator and AI Engineer, discusses Retrieval Augmented Generation (RAG), Semantic Vector Searches, and Vector Databases reshaping data landscapes. Topics include AI model deployment complexities, AI evaluation frameworks, challenges in client approval, and struggles with data ingestion in AI environments.

Senior Machine Learning Engineer at Spotify, Sanket Gupta, discusses foundational embeddings for transfer learning in recommender systems. Topics include large-scale recommender system building, transfer learning with user and item embeddings, system evaluation, and MLOps challenges. They explore music recommendation intricacies, user behavior analysis challenges, and balancing real-time recommendations with scalability. The podcast delves into user representations, cross-content embeddings, and maintaining content freshness for optimal user experiences.

CEO and Founder Ryan Carson discusses democratizing AI development and the impact of new technologies like Gaudi three. He emphasizes the importance of aligning individual work with company goals and the potential benefits of AI in professional interactions. The conversation also touches on practical AI applications, entrepreneurship, and the role of technology in shaping the future.

Salman Avestimehr, CEO & Founder of FEDML, discusses FEDML Nexus AI, an enterprise AI platform enabling generative AI applications at scale. Topics include challenges in AI platform development, ownership, scalability, integrating with cloud infrastructure, evaluating language models, small scale foundation models in federated learning, and advantages of using small models for AI agents on mobile phones.

Mohamed Elgendy, Co-Founder & CEO at Kolena, discusses AI Quality with a focus on tailored quality standards, risk management, and edge deployments. The podcast emphasizes the formation of gold standards for AI, collaboration among AI builders, regulators, and infrastructure firms, and the need for diversification in the tech industry. Elgendy's AI Quality Conference aims to set effective but innovation-friendly quality standards for AI.

Simon Karasik, an experienced ML Engineer, discusses handling multi-terabyte LLM checkpoints. Topics include managing massive models, cloud storage options, comparing Slurm and Kubernetes, navigating data processing challenges, monitoring Kubernetes nodes with faulty GPUs, and simplifying model training processes.