AI Evangelist Alex Volkov shares his journey from running AI models to founding an AI startup. Explore topics like multimodal transformer architecture for lucid dreaming, challenges in MLOps with video data, and evaluating AI models with 'vibe checks'. Learn about AI agents, automation in content creation, and fostering community engagement in AI development.

Experts discuss practical applications of Large Language Models in customer service, property management, and paper summarization. They explore AI's impact on product development, the importance of clear communication, and maximizing workflow efficiency for revenue growth.

The podcast with Daniel Svonava discusses the use of vector embeddings in information retrieval, optimizing recommender systems with vector compute, customizing search vectors for relevance, and the efficiency of specialized models. It explores vector databases, deep learning-based retrieval challenges, and the transformative power of vector embeddings in diverse applications.

Morgan McGuire and Anish Shah discuss the challenges of productionizing large language models, including cost optimization, latency requirements, trust of output, and debugging. They also mention an upcoming AI in Production Conference on February 22 with informative workshops.

Alexandra Diem, Head of Cloud Analytics & MLOps at Gjensidige, discusses challenges of generative AI in sensitive data environments, specialized chatbots, data governance, enabling teams through MVP development, transitioning analysts into data scientists, and the importance of collaboration. Her journey from academia to being a consultant in Norway is also explored.

Aayush Mudgal, Senior Machine Learning Engineer at Pinterest, discusses the evolution of ads ranking at Pinterest, including transitioning to deep learning-based transformer models. Topics covered include challenges in productionizing large language models, transitioning to deep learning models, incorporating sequential signals, multi-task learning, and transfer learning, scaling machine learning at Pinterest, and the use of transformers in ad rankings and recommendation models.

The podcast discusses the complexities of Language Model evaluation, the use of open-source versus private models, and the urgency of getting models into production. It also explores the challenges of evaluating LLM outcomes and highlights the importance of prompt engineering. Additionally, it emphasizes the need to quickly get ML models into production for identifying bottlenecks and setting up metrics.

Guests Matt Bleifer and Mike Eastham from Tecton discuss the challenges and use cases of Large Language Models and feature platforms in MLOps. They also introduce Tecton's new product RIFT, highlight the importance of choosing the right tool for the job, and delve into the design decisions and challenges of data processing and aggregation in a managed service.

QuantumBlack and McKinsey discuss the trade-offs of buying vs building GenAI solutions, including considerations of black box solutions and transparency. They explore the roles of traditional AI and JAN-AI in messaging channels and the generative nature of AI. The challenges and considerations in using APIs for machine learning models are also discussed.

Jon Cooke, founder of Dataception and creator of the Data Product Pyramid, discusses using specialist small language models and graphs to accelerate data product ecosystems. Topics include deconstructed Encoder/Decoder Transformers, data product management, tech to eliminate data grunt work, and building sophisticated analytics in real-time.