MLOps.community

A podcast by Demetrios Brinkmann

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379 Episodes

  1. The Only Constant is (Data) Change // Panel // DE4AI

    Published: 11/10/2024
  2. The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267

    Published: 9/10/2024
  3. Making Your Company LLM-native // Francisco Ingham // #266

    Published: 6/10/2024
  4. Unpacking 3 Types of Feature Stores // Simba Khadder // #265

    Published: 1/10/2024
  5. Reinvent Yourself and Be Curious // Stefano Bosisio // MLOps Podcast #264

    Published: 27/09/2024
  6. Global Feature Store // Gottam Sai Bharath & Cole Bailey // #263

    Published: 24/09/2024
  7. RAG Quality Starts with Data Quality // Adam Kamor // #262

    Published: 20/09/2024
  8. Who's MLOps for Anyway? // Jonathan Rioux // #261

    Published: 17/09/2024
  9. Alignment is Real // Shiva Bhattacharjee // #260

    Published: 13/09/2024
  10. Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259

    Published: 11/09/2024
  11. Building in Production Human-centred GenAI Solutions // Mohamed Abusaid & Mara Pometti// #177

    Published: 5/09/2024
  12. Visualize - Bringing Structure to Unstructured Data // Markus Stoll // #258

    Published: 3/09/2024
  13. AI Testing Highlights // Special MLOps Podcast Episode

    Published: 1/09/2024
  14. MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257

    Published: 30/08/2024
  15. MLOps for GenAI Applications // Harcharan Kabbay // #256

    Published: 27/08/2024
  16. BigQuery Feature Store // Nicolas Mauti // #255

    Published: 23/08/2024
  17. Design and Development Principles for LLMOps // Andy McMahon // #254

    Published: 20/08/2024
  18. Data Quality = Quality AI // AIQCON Panel

    Published: 16/08/2024
  19. The Variational Book // Yuri Plotkin // #253

    Published: 13/08/2024
  20. Vision and Strategies for Attracting & Driving AI Talents in High Growth // Panel // AIQCON

    Published: 9/08/2024

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Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.

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