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DataTalks.Club

DataTalks.Club

AI
更新于 2026-05-15 01:27 共 100 条
  1. 1 Competitions: Beyond the Kaggle Leaderboard - Tatiana Habruseva ↗
  2. 2 PyConDE 2026 Conference Interviews ↗
  3. 3 Starting a Data Conference: The Data Makers Fest Story - Leonid Kholkine ↗
  4. 4 Understanding the AI Engineer Role - Nasser Qadri ↗
  5. 5 Data Engineer Career in 2026: Roles, Specializations, and What Companies Look for - Slawomir Tulski ↗
  6. 6 Inside the AI Engineer Role: Tools, Skills, and Career Path - Ruslan Shchuchkin ↗
  7. 7 How to Become an AI Engineer After a Career Break - Revathy Ramalingam ↗
  8. 8 The Future of AI Agents - Aditya Gautam ↗
  9. 9 Foundations of Analytics Engineer Role: Skills, Scope, and Modern Practices - Juan Manuel Perafan ↗
  10. 10 AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products - Paul Iusztin ↗
  11. 11 Applying ML: An Ongoing Personal Journey ↗
  12. 12 Building Pet Health Tech: ML, Sensors, and Dog Behavior Data ↗
  13. 13 From Full-Time Mom to Head of Data and Cloud - Xia He-Bleinagel ↗
  14. 14 From Black-Box Systems to Augmented Decision-Making - Anusha Akkina ↗
  15. 15 Qdrant 2025 Conference Interviews ↗
  16. 16 How to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-Anderson ↗
  17. 17 From Biotechnology to Bioinformatics Software - Sebastian Ayala Ruano ↗
  18. 18 Lessons from Applied AI: Tesla, Waymo, and Beyond - Aishwarya Jadhav ↗
  19. 19 Building reliable AI products in the era of Gen AI and Agents - Ranjitha Kulkarni ↗
  20. 20 From Theme Parks to Tesla: Building Data Products That Work ↗
  21. 21 From Semiconductors to Machine Learning: A Career in Data and Teaching ↗
  22. 22 Lessons from Two Decades of AI - Micheal Lanham ↗
  23. 23 Berlin PyData 2025 Conference Interviews ↗
  24. 24 From Astronomy to Applied ML - Daniel Egbo ↗
  25. 25 Berlin Buzzwords 2025 Conference Interviews ↗
  26. 26 From Medicine to Machine Learning: How Public Learning Turned into a Career - Pastor Soto ↗
  27. 27 How to Rebuild Data Trust? Mindful Data Strategy and Maintenance vs Innovation - Lior Barak ↗
  28. 28 From Simulations to Freelance Data Engineering: Orell's Journey Out of Academia and Into Consulting - Orell Garten ↗
  29. 29 Can You Quit Your Job and Still Succeed as a Data Freelancer? ↗
  30. 30 From Hackathons to Developer Advocacy - Will Russel ↗
  31. 31 Build a Strong Career in Data - Lavanya Gupta ↗
  32. 32 From Supply Chain Management to Digital Warehousing and FinOps - Eddy Zulkifly ↗
  33. 33 Data Intensive AI - Bartosz Mikulski ↗
  34. 34 MLOps in Corporations and Startups - Nemanja Radojkovic ↗
  35. 35 Trends in Data Engineering – Adrian Brudaru ↗
  36. 36 Competitive Machine Leaning And Teaching – Alexander Guschin ↗
  37. 37 Redefining AI Infrastructure: Open-Source, Chips, and the Future Beyond Kubernetes – Andrey Cheptsov ↗
  38. 38 Linguistics and Fairness - Tamara Atanasoska ↗
  39. 39 Career choices, transitions and promotions in and out of tech - Agita Jaunzeme ↗
  40. 40 Career advice, learning, and featuring women in ML and AI - Isabella Bicalho ↗
  41. 41 AI in Industry: Trust, Return on Investment and Future - Maria Sukhareva ↗
  42. 42 Large Hadron Collider and Mentorship – Anastasia Karavdina ↗
  43. 43 MLOps as a Team - Raphaël Hoogvliets ↗
  44. 44 Using Data to Create Liveable Cities - Rachel Lim ↗
  45. 45 DataTalks.Club 4th Anniversary AMA Podcast – Alexey Grigorev and Johanna Bayer ↗
  46. 46 Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak ↗
  47. 47 DataOps, Observability, and The Cure for Data Team Blues - Christopher Bergh ↗
  48. 48 Working as a Core Developer in the Scikit-Learn Universe - Guillaume Lemaître ↗
  49. 49 Building a Domestic Risk Assessment Tool - Sabina Firtala ↗
  50. 50 Berlin Buzzwords 2024 ↗
  51. 51 Community Building and Teaching in AI & Tech - Erum Afzal ↗
  52. 52 Working in Open Source - Probabl.ai and sklearn - Vincent Warmerdam ↗
  53. 53 AI for Ecology, Biodiversity, and Conservation - Tanya Berger-Wolf ↗
  54. 54 Knowledge Graphs and LLMs Across Academia and Industry - Anahita Pakiman ↗
  55. 55 Inclusive Data Leadership Coaching - Tereza Iofciu ↗
  56. 56 Building Production Search Systems - Daniel Svonava ↗
  57. 57 Building Machine Learning Products - Reem Mahmoud ↗
  58. 58 Make an Impact Through Volunteering Open Source Work - Sara EL-ATEIF ↗
  59. 59 Accelerating The Job Hunt for The Perfect Job in Tech - Sarah Mestiri ↗
  60. 60 Machine Learning Engineering in Finance - Nemanja Radojkovic ↗
  61. 61 Stock Market Analysis with Python and Machine Learning - Ivan Brigida ↗
  62. 62 Bayesian Modeling and Probabilistic Programming - Rob Zinkov ↗
  63. 63 Navigating Challenges and Innovations in Search Technologies - Atita Arora ↗
  64. 64 The Entrepreneurship Journey: From Freelancing to Starting a Company - Adrian Brudaru ↗
  65. 65 Become a Data Freelancer - Dimitri Visnadi ↗
  66. 66 AI for Digital Health - Maria Bruckert ↗
  67. 67 Cracking the Code: Machine Learning Made Understandable - Christoph Molnar ↗
  68. 68 The Unwritten Rules for Success in Machine Learning - Jack Blandin ↗
  69. 69 From a Research Scientist at Amazon to a Machine learning/AI Consultant - Verena Webber ↗
  70. 70 From Marketing to Product Owner in Search - Lera Kaimashnіkova ↗
  71. 71 Collaborative Data Science in Business - Ioannis Mesionis ↗
  72. 72 Bridging Data Science and Healthcare - Eleni Stamatelou ↗
  73. 73 DataTalks.Club Anniversary Interview - Alexey Grigorev, Johanna Bayer ↗
  74. 74 Data Engineering for Fraud Prevention - Angela Ramirez ↗
  75. 75 From Data Manager to Data Architect - Loïc Magnien ↗
  76. 76 Pragmatic and Standardized MLOps - Maria Vechtomova ↗
  77. 77 Democratizing Causality - Aleksander Molak ↗
  78. 78 Mastering Data Engineering as a Remote Worker - José María Sánchez Salas ↗
  79. 79 The Good, the Bad and the Ugly of GPT - Sandra Kublik ↗
  80. 80 LLMs for Everyone - Meryem Arik ↗
  81. 81 Investing in Open-Source Data Tools - Bela Wiertz ↗
  82. 82 Why Machine Learning Design is Broken - Valerii Babushkin ↗
  83. 83 Interpretable AI and ML - Polina Mosolova ↗
  84. 84 From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner ↗
  85. 85 From MLOps to DataOps - Santona Tuli ↗
  86. 86 Data Developer Relations - Hugo Bowne-Anderson ↗
  87. 87 Lessons Learned from Freelancing and Working in a Start-up - Antonis Stellas ↗
  88. 88 Data Access Management - Bart Vandekerckhove ↗
  89. 89 Data Strategy: Key Principles and Best Practices - Boyan Angelov ↗
  90. 90 Practical Data Privacy - Katharine Jarmul ↗
  91. 91 Building Scalable and Reliable Machine Learning Systems - Arseny Kravchenko ↗
  92. 92 Building an Open-Source NLP Tool - Johannes Hötter ↗
  93. 93 Navigating Industrial Data Challenges - Rosona Eldred ↗
  94. 94 Mastering Self-Learning in Machine Learning - Aaisha Muhammad ↗
  95. 95 The Secret Sauce of Data Science Management - Shir Meir Lador ↗
  96. 96 SE4ML - Software Engineering for Machine Learning - Nadia Nahar ↗
  97. 97 Starting a Consultancy in the Data Space - Aleksander Kruszelnicki ↗
  98. 98 Biohacking for Data Scientists and ML Engineers - Ruslan Shchuchkin ↗
  99. 99 Analytics for a Better World - Parvathy Krishnan ↗
  100. 100 Accelerating the Adoption of AI through Diversity - Dânia Meira ↗