ML Platform Podcast
neptune.ai
Get behind-the-scenes insights into the world of internal ML platforms and MLOps stack components with Piotr Niedźwiedź and Aurimas Griciūnas in their show, where together with ML platform professionals, they discuss design choices, best practices, and real-world solutions to MLOps challenges. Brought to you by neptune.ai.
Categories: Technology
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Listen to the last episode:
On this episode of the ML Platform Podcast, we have Olalekan Elesin as our guest. Olalekan shares insights on building the ML Platform at Scout24, including problems to be solved, reasons for going multi-cloud, point solutions vs. end-to-end platforms, data platforms vs. ML platforms, iterative product development approach, and more.
Previous episodes
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36 - Building Internal ML Platform at Scout24: How to Ship Features that People Actually NeedFri, 26 Apr 2024
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35 - Building ML Platform at Uber, Feature Stores, Vector Databases, and Real-time Feature ManagementFri, 12 Apr 2024
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34 - Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024Fri, 22 Dec 2023
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33 - Building MLOps Capabilities at GitLab As a One-Person ML Platform Team With Eduardo BonetWed, 06 Sep 2023
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32 - Learnings From Building the ML Platform at Mailchimp With Mikiko BazeleyWed, 09 Aug 2023
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31 - Learnings From Building the ML Platform at Stitch Fix With Stefan KrawczykWed, 12 Jul 2023
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30 - Navigating Organizational Barriers by Doing MLOps with Leanne Kim FitzpatrickThu, 25 May 2023
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29 - Tackling MLOps Challenges in Computer Vision with Marcin TuszyńskiTue, 09 May 2023
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28 - What Does GPT-3 Mean For the Future of MLOps? with David HersheyWed, 26 Apr 2023
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27 - Managing Data and ML Teams to Deliver Value with Delina IvanovaWed, 12 Apr 2023
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26 - Leveraging MLOps Technologies and Principles at Non-ML Companies with Andreas Malekos and Ivan Chon-Hon ChanWed, 29 Mar 2023
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25 - Doing MLOps for Clinical Research Studies with Silas Bempong and Abhijit RameshWed, 15 Mar 2023
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24 - Deploying Conversational AI Products to Production with Jason FlaksWed, 01 Mar 2023
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23 - Implementing Vector Search Engines with Kacper LukawskiWed, 15 Feb 2023
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22 - ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr NiedźwiedźWed, 01 Feb 2023
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21 - Continuous MLOps Pipelines with Itay Ben HaimWed, 18 Jan 2023
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20 - Setting Up MLOps at a Healthcare Startup with Vishnu RachakondaWed, 04 Jan 2023
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19 - Intersecting DevOps With the ML Lifecycle with Shirsha Ray ChaudhuriWed, 21 Dec 2022
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18 - Writing Clean, Production-Level ML Code with Laszlo SragnerWed, 07 Dec 2022
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17 - Differences Between Shipping Classic Software and Operating ML Models with a Lead MLOps Engineer at TMNL Simon Stiebellehner, and neptune.ai CEO Piotr NiedzwiedzWed, 23 Nov 2022
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16 - Building Well-Architected Machine Learning Solutions on AWS with Phil BasfordWed, 09 Nov 2022
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15 - Solving the Model Serving Component of the MLOps Stack with Chaoyu YangWed, 26 Oct 2022
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14 - How early-stage startups and small teams tackle MLOps with Duarte CarmoWed, 12 Oct 2022
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13 - AutoML and MLOps with Adam BeckerWed, 28 Sep 2022
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12 - Embracing Responsible AI for ML Models in Production with Amber RobertsWed, 14 Sep 2022
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11 - Building an MLOps Culture in Your Team with Adam SrokaWed, 31 Aug 2022
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10 - Your First MLOps System: What Does Good Look Like? with Andy McMahonWed, 17 Aug 2022
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9 - Leveraging Unlabeled Image Data with Self-Supervised Learning or Pseudo Labeling with Mateusz OpalaWed, 03 Aug 2022
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8 - Managing Computer Vision Projects with Michal TadeusiakWed, 20 Jul 2022
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7 - Data Engineering and MLOps for Neural Search with Fernando Rejon Barrera and Jakub ZavrelWed, 06 Jul 2022
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6 - Navigating ML Observability with Danny LeybzonWed, 22 Jun 2022
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5 - Testing Recommender Systems with Federico BianchiWed, 08 Jun 2022
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4 - Deploying models on GPU with Kyle MorrisWed, 25 May 2022
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3 - Building a Visual Search Engine with Kuba CieslikWed, 11 May 2022
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2 - MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo TagliabueWed, 11 May 2022
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