Agentic Conversations (formally mlops.community)

Episodes

• Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325

  Jun 20, 2025

  Not summarized yet

• The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324

  Jun 17, 2025

  Not summarized yet

• Everything Hard About Building AI Agents Today

  Jun 13, 2025

  Not summarized yet

• Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318

  Jun 11, 2025

  Not summarized yet

• Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322

  Jun 10, 2025

  Not summarized yet

• Hard Learned Lessons from Over a Decade in AI

  Jun 6, 2025

  Production-ready machine learning systems significantly enhance a company's ability to automate decision-making and drive revenue.

• Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320

  Jun 3, 2025

  There is a strong correlation between product metrics and evaluation metrics (eVALs) for AI systems.

• Getting AI Apps Past the Demo // Vaibhav Gupta // #319

  May 30, 2025

  BAML is designed to improve prompt engineering for AI by providing a structured environment that challenges the existing demo-style coding practices.

• Building Out GPU Clouds // Mohan Atreya // #317

  May 23, 2025

  GPU procurement presents significant challenges, including availability and understanding specific task requirements.

• A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live

  May 21, 2025

  Understanding the definition and implementation of agents is crucial for leveraging AI tools effectively.

• AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315

  May 16, 2025

  Every entrepreneur faces challenges and slumps; overcoming these is part of the journey.

• AI, Marketing, and Human Decision Making // Fausto Albers // #313

  May 14, 2025

  Generative AI is enhancing the marketing landscape by allowing real-time adjustments to creative content based on performance analysis.

• MLOps with Databricks // Maria Vechtomova // #314

  May 13, 2025

  Databricks has emerged as a leading platform for MLOps due to its comprehensive features and ease of integration.

• Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312

  May 6, 2025

  Empirical testing in AI systems is crucial; don't just take established norms at face value.

• Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311

  May 2, 2025

  Jailbreaking AI by manipulating prompts raises ethical concerns and risks.

• GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // Paco Nathan & Weidong Yang // #310

  Apr 29, 2025

  GraphBI represents a significant advancement in how organizations can leverage combined data sources for analytics.

• AI Data Engineers - Data Engineering After AI // Vikram Chennai // #309

  Apr 25, 2025

  AI Data Engineers are reducing the workload on traditional data engineers by automating complex tasks.

• I Am Once Again Asking "What is MLOps?" // Oleksandr Stasyk // #308

  Apr 22, 2025

  MLOps is increasingly crucial in ensuring operational excellence amidst the rapid development of AI technologies.

• How Sama is Improving ML Models to Make AVs Safer // Duncan Curtis // #307

  Apr 18, 2025

  The quality of data is crucial to the effectiveness of AI models, especially in complex applications like autonomous driving.

• Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // Luca Fiaschi // #306

  Apr 15, 2025

  The role of ML is critical in managing inventory and demand for businesses in sectors like food and fashion, where freshness and timeliness are crucial.