In this podcast, Niels Bantilan, Chief Machine Learning Engineer at Union, discusses the role of infrastructure in ML leveraging open source. Topics covered include data quality, schema definition, integrating tools like Polars, tracking data quality over time, generative DevOps, reproducibility in MLOps, and navigating edge cases.

A discussion with Venky Ganti, CEO of Numbers Station, and Laurel Orr about the merits and limitations of 'one-size fits all' LLMs, the contrast between general LLMs and verticalized models, the significance of ownership structures, and the future of LLMs. They also explore the challenges of hosting private LLMs, AI's role in data wrangling, customizing LLMs for complex data scenarios, the evolution of LLMs and the need for customization, and evaluation methods for text-to-sequel models.

Join the Weights & Biases round-table discussion on model management in regulated environments. Explore challenges in compliance and effective strategies. Learn about tracking and documenting the model's journey, avoiding undesirable outcomes, open source LLMs, challenges of using Weights & Biases in finance, E-vowels as a service, organization structure, and the value of expertise.

Varun Mohan, CEO of Codeium, discusses the challenges of building generative AI applications and the development of Codeium, a product designed for software developers. They also talk about the importance of focusing on coding in software development and future-proofing work in the AI space.

Explore transformative role of AI in EdTech, challenges in AI integration, strategies for building differentiated business model. Discuss experiments with AI in education, challenges of personalized learning, Brainly and Duolingo's AI personalization. Managing expectations and leveraging unique data in AI education. Approach to creating features for AI system in education, importance of understanding learners and curriculum. AI in education: initial skepticism, growing acceptance, problems in current education system, optimism about using AI to improve learning opportunities.

Tecton, MLOps experts with over 35 years of combined experience, discuss challenges in deploying ML models, evaluating ROI, bridging the gap between batch and streaming systems, maintaining consistency in processing, monitoring feature quality, building recommendation systems, and quantifying ML project value.

Omar Khattab, PhD Candidate at Stanford, discusses DSPy, a programming model that optimizes language model pipelines. Topics include the drawbacks of prompt-based approaches, fine-tuning modules, retrieval-based NLP systems, BERT in pipelines, and the concept of fine tuning in language models.

Guests include Martian, building a model router for LLMs, Corti using AI to improve healthcare, and Transforms pioneering in synthetic entertainment using AI. Topics discussed include dynamic routing of prompts, improving patient care, transforming media, simplifying LLM usage, incumbents in the LLM space, and advice in the field of machine learning.

"MLOps Podcast #193" features Pierre Salvy, Head of Engineering at Cambrium. He discusses using AI to design proteins, creating a vegan collagen for cosmetics, lab-grown meats, molecular cheese, and less harmful plastics. They also touch on the cost of experiments, ski experiences, and the challenges of transitioning to a managerial role.

Panel discussion on product engineering for language models and the challenges of asynchronous communication. Topics include the successes and challenges of L1 powered products, the revolution in natural language processing, challenges of asynchronous communication in notifications, and the utility of multi-modal AI.