MLOps.community

Guests Patrick Barker, Founder / CTO of Kentauros AI and Farhood Etaati, Software Engineer at Yektanet, discuss the challenges and skepticism surrounding MLOps and its relation to previous ML models. They explore the potential future developments, the significance of knowledge transfer, and the emergence of persona-specific tools. The speakers also mention building a tool for TypeScript developers and discuss the challenges faced by MLOps engineers.

Aleksa Gordić, ex-Google DeepMind/Microsoft ML engineer, discusses pioneering AI models for regional languages, focusing on the development of YugoGPT. They explore unique language dynamics in the Balkans, business opportunities for multilingual models, and challenges in deploying large language models. The conversation delves into Aleksa's experience with vision and image models, collaborations with tech players, and use of advanced technologies. They also discuss open sourcing models, the lack of language support, and advantages/limitations of working for a company.

Navigate data management intricacies with Hannes Mühleisen and Jordan Tigani, the creators of DuckDB and MotherDuck. They discuss the controversial 'hallucinate' shirt, the origins of DuckDB and Oracle, CSV reading in data systems, addressing pain points, embracing community feedback, and the unique features and potential of DuckDB

Paco Nathan, Managing Partner at Derwen, Inc., talks about key findings from conferences, commonalities among teams with ROI on ML in production, leveraging existing resources and domain expertise in AI, the importance of software engineering and ops in AI, and the need to regulate AGI and implement universal basic income.

Guest Mihail Eric, co-founder of Storia AI, discusses the significance of human sentiment in an AI-driven era. They explore the evolution and impact of image generation models, the challenges of MLOps, and the importance of developer-friendly APIs. They also discuss fundraising struggles, the need for solid foundations in image generation, and the vision of creating generalized building blocks for different industries.

Nathan Ryan Frank, former astrophysicist turned data scientist and machine learning engineer, discusses challenges and solutions when operationalizing machine learning systems. Topics include team dynamics, communication issues, approaching MLOps from a DevOps perspective, and practical guidance for newcomers to MLOps. The podcast also delves into the speaker's experience building telescopes, transitioning to industry and basketball analytics, and the importance of testing in software development. They also share thrilling hiking adventures.

Listen to Nick Hasty, Director of Product, Discovery & Machine Learning at Giphy, discuss the nature of creativity and its relationship to intelligence. Explore the transition from GIFs to stickers, the impact of L.L.M.s on shipping ML products, the decision-making process for using language models, combining intuition and data in AI product development, insights and challenges in helping startups and leading teams, and the paradoxes in the AI industry.

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.