ML Platform Podcast

36 - Building Internal ML Platform at Scout24: How to Ship Features that People Actually Need

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.

Altri episodi

36 - Building Internal ML Platform at Scout24: How to Ship Features that People Actually Need

Fri, 26 Apr 2024
35 - Building ML Platform at Uber, Feature Stores, Vector Databases, and Real-time Feature Management

Fri, 12 Apr 2024
34 - Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024

Fri, 22 Dec 2023
33 - Building MLOps Capabilities at GitLab As a One-Person ML Platform Team With Eduardo Bonet

Wed, 06 Sep 2023
32 - Learnings From Building the ML Platform at Mailchimp With Mikiko Bazeley

Wed, 09 Aug 2023
31 - Learnings From Building the ML Platform at Stitch Fix With Stefan Krawczyk

Wed, 12 Jul 2023
30 - Navigating Organizational Barriers by Doing MLOps with Leanne Kim Fitzpatrick

Thu, 25 May 2023
29 - Tackling MLOps Challenges in Computer Vision with Marcin Tuszyński

Tue, 09 May 2023
28 - What Does GPT-3 Mean For the Future of MLOps? with David Hershey

Wed, 26 Apr 2023
27 - Managing Data and ML Teams to Deliver Value with Delina Ivanova

Wed, 12 Apr 2023
26 - Leveraging MLOps Technologies and Principles at Non-ML Companies with Andreas Malekos and Ivan Chon-Hon Chan

Wed, 29 Mar 2023
25 - Doing MLOps for Clinical Research Studies with Silas Bempong and Abhijit Ramesh

Wed, 15 Mar 2023
24 - Deploying Conversational AI Products to Production with Jason Flaks

Wed, 01 Mar 2023
23 - Implementing Vector Search Engines with Kacper Lukawski

Wed, 15 Feb 2023
22 - ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

Wed, 01 Feb 2023
21 - Continuous MLOps Pipelines with Itay Ben Haim

Wed, 18 Jan 2023
20 - Setting Up MLOps at a Healthcare Startup with Vishnu Rachakonda

Wed, 04 Jan 2023
19 - Intersecting DevOps With the ML Lifecycle with Shirsha Ray Chaudhuri

Wed, 21 Dec 2022
18 - Writing Clean, Production-Level ML Code with Laszlo Sragner

Wed, 07 Dec 2022
17 - Differences Between Shipping Classic Software and Operating ML Models with a Lead MLOps Engineer at TMNL Simon Stiebellehner, and neptune.ai CEO Piotr Niedzwiedz

Wed, 23 Nov 2022
16 - Building Well-Architected Machine Learning Solutions on AWS with Phil Basford

Wed, 09 Nov 2022
15 - Solving the Model Serving Component of the MLOps Stack with Chaoyu Yang

Wed, 26 Oct 2022
14 - How early-stage startups and small teams tackle MLOps with Duarte Carmo

Wed, 12 Oct 2022
13 - AutoML and MLOps with Adam Becker

Wed, 28 Sep 2022
12 - Embracing Responsible AI for ML Models in Production with Amber Roberts

Wed, 14 Sep 2022
11 - Building an MLOps Culture in Your Team with Adam Sroka

Wed, 31 Aug 2022
10 - Your First MLOps System: What Does Good Look Like? with Andy McMahon

Wed, 17 Aug 2022
9 - Leveraging Unlabeled Image Data with Self-Supervised Learning or Pseudo Labeling with Mateusz Opala

Wed, 03 Aug 2022
8 - Managing Computer Vision Projects with Michal Tadeusiak

Wed, 20 Jul 2022
7 - Data Engineering and MLOps for Neural Search with Fernando Rejon Barrera and Jakub Zavrel

Wed, 06 Jul 2022
6 - Navigating ML Observability with Danny Leybzon

Wed, 22 Jun 2022
5 - Testing Recommender Systems with Federico Bianchi

Wed, 08 Jun 2022
4 - Deploying models on GPU with Kyle Morris

Wed, 25 May 2022
3 - Building a Visual Search Engine with Kuba Cieslik

Wed, 11 May 2022
2 - MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue

Wed, 11 May 2022

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