ML Platform Podcast
By neptune.ai
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Episodes: 36
Description
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.
| Episode | Date |
|---|---|
|
Going Deep On Model Serving, Deploying LLMs and Anything Production-Deployment
|
May 10, 2024 |
|
Building Internal ML Platform at Scout24: How to Ship Features that People Actually Need
|
Apr 26, 2024 |
|
Building ML Platform at Uber, Feature Stores, Vector Databases, and Real-time Feature Management
|
Apr 12, 2024 |
|
Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024
|
Dec 22, 2023 |
|
Building MLOps Capabilities at GitLab As a One-Person ML Platform Team With Eduardo Bonet
|
Sep 06, 2023 |
|
Learnings From Building the ML Platform at Mailchimp With Mikiko Bazeley
|
Aug 09, 2023 |
|
Learnings From Building the ML Platform at Stitch Fix With Stefan Krawczyk
|
Jul 12, 2023 |
|
Navigating Organizational Barriers by Doing MLOps with Leanne Kim Fitzpatrick
|
May 25, 2023 |
|
Tackling MLOps Challenges in Computer Vision with Marcin Tuszyński
|
May 09, 2023 |
|
What Does GPT-3 Mean For the Future of MLOps? with David Hershey
|
Apr 26, 2023 |
|
Managing Data and ML Teams to Deliver Value with Delina Ivanova
|
Apr 12, 2023 |
|
Leveraging MLOps Technologies and Principles at Non-ML Companies with Andreas Malekos and Ivan Chon-Hon Chan
|
Mar 29, 2023 |
|
Doing MLOps for Clinical Research Studies with Silas Bempong and Abhijit Ramesh
|
Mar 15, 2023 |
|
Deploying Conversational AI Products to Production with Jason Flaks
|
Mar 01, 2023 |
|
Implementing Vector Search Engines with Kacper Lukawski
|
Feb 15, 2023 |
|
ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź
|
Feb 01, 2023 |
|
Continuous MLOps Pipelines with Itay Ben Haim
|
Jan 18, 2023 |
|
Setting Up MLOps at a Healthcare Startup with Vishnu Rachakonda
|
Jan 04, 2023 |
|
Intersecting DevOps With the ML Lifecycle with Shirsha Ray Chaudhuri
|
Dec 21, 2022 |
|
Writing Clean, Production-Level ML Code with Laszlo Sragner
|
Dec 07, 2022 |
|
Differences Between Shipping Classic Software and Operating ML Models with a Lead MLOps Engineer at TMNL Simon Stiebellehner, and neptune.ai CEO Piotr Niedzwiedz
|
Nov 23, 2022 |
|
Building Well-Architected Machine Learning Solutions on AWS with Phil Basford
|
Nov 09, 2022 |
|
Solving the Model Serving Component of the MLOps Stack with Chaoyu Yang
|
Oct 26, 2022 |
|
How early-stage startups and small teams tackle MLOps with Duarte Carmo
|
Oct 12, 2022 |
|
AutoML and MLOps with Adam Becker
|
Sep 28, 2022 |
|
Embracing Responsible AI for ML Models in Production with Amber Roberts
|
Sep 14, 2022 |
|
Building an MLOps Culture in Your Team with Adam Sroka
|
Aug 31, 2022 |
|
Your First MLOps System: What Does Good Look Like? with Andy McMahon
|
Aug 17, 2022 |
|
Leveraging Unlabeled Image Data with Self-Supervised Learning or Pseudo Labeling with Mateusz Opala
|
Aug 03, 2022 |
|
Managing Computer Vision Projects with Michal Tadeusiak
|
Jul 20, 2022 |
|
Data Engineering and MLOps for Neural Search with Fernando Rejon Barrera and Jakub Zavrel
|
Jul 06, 2022 |
|
Navigating ML Observability with Danny Leybzon
|
Jun 22, 2022 |
|
Testing Recommender Systems with Federico Bianchi
|
Jun 08, 2022 |
|
Deploying models on GPU with Kyle Morris
|
May 25, 2022 |
|
MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue
|
May 11, 2022 |
|
Building a Visual Search Engine with Kuba Cieslik
|
May 11, 2022 |