# The platform team

161 sessions · follows the tags platform-teams, developer-experience
Page: https://mlopstalks.com/threads/the-platform-team

Who builds the platform data scientists use, and how big should that team be? Then comes the recurring discovery that building it does not make anyone use it.

## 2020

21 sessions.

- [Running a Fintech on Machine Learning](https://mlopstalks.com/talks/running-a-fintech-on-machine-learning) (Caique Lima & Cristiano Breuel, Nubank). Pushed back: The team favored building its feature store internally rather than buying an existing product because it wanted tighter integration and customization, and found no sufficiently mature market solution. [46:45](https://www.youtube.com/watch?v=ry_P5D_d7XA&t=2805s)
- [Build vs Buy an ML Platform](https://mlopstalks.com/talks/build-vs-buy-an-ml-platform) (Diego Oppenheimer, Algorithmia). Pushed back: Diego Oppenheimer rejects the assumption that buying a platform necessarily removes flexibility, comparing it with the mix of purchased tools used in software development. [40:57](https://www.youtube.com/watch?v=1bHQE11Qq0k&t=2457s)
- [Scaling ML Capabilities in Large Organizations](https://mlopstalks.com/talks/scaling-ml-capabilities-in-large-organizations) (Bertjan Broeksema & Axel Goblet, BigData Republic). Pushed back: Joe argues that organizations should avoid buying a model serving platform when a simpler, opinionated solution is sufficient. [57:25](https://www.youtube.com/watch?v=aeYnfU26WGk&t=3445s)
- [How to Choose the Right ML Tool](https://mlopstalks.com/talks/how-to-choose-the-right-ml-tool) (Jose Navarro, Cookpad & Mariya Davydova, Neu.ro). Pushed back: Mariya Davydova recommends building an internal MVP in the Feast situation, while the team is also considering waiting for the planned release or choosing another tool. [33:31](https://www.youtube.com/watch?v=mmTCGkm3ZoQ&t=2011s)

17 more from 2020 on this thread: https://mlopstalks.com/threads/the-platform-team/2020

## 2021

40 sessions.

- [2 tools = 90% operational ML](https://mlopstalks.com/talks/2-tools-90-operational-ml) (Michael Del Balso, Tecton & Willem Pienaar, Feast & David Aronchick, Kubeflow). Pushed back: The panel distinguished Kubernetes as a common infrastructure substrate from Kubeflow as the machine-learning platform built on it. [33:05](https://www.youtube.com/watch?v=iWMQxCGFdU0&t=1985s)
- [MLOps Engineering Labs Recap, Part 2](https://mlopstalks.com/talks/mlops-engineering-labs-recap-part-2) (Laszlo Sranger & Artem Yushkovsky, Neu.ro & Paulo Maia, Nilgai). Pushed back: Laszlo viewed direct Kubernetes use as too difficult for an average data scientist, while Artem argued that Kubernetes is highly useful and should be exposed through simpler platforms. [39:23](https://www.youtube.com/watch?v=iN8aC1BYl5A&t=2363s)
- [MLOps Investments](https://mlopstalks.com/talks/mlops-investments) (Sarah Catanzaro, Amplify Partners). Pushed back: Sarah Catanzaro does not favor end-to-end ML platforms as a general solution because they can be poorly suited to particular workflows, while point-product sprawl also creates maintenance and integration costs. [30:24](https://www.youtube.com/watch?v=twvHm8Fa5jk&t=1824s)
- [Maturing Machine Learning in Enterprise](https://mlopstalks.com/talks/maturing-machine-learning-in-enterprise) (Kyle Gallatin, Etsy). Pushed back: Kyle Gallatin rejects the idea that one platform can fit every machine learning use case and argues for integration-first platforms with room for customization. [17:02](https://www.youtube.com/watch?v=kfm3Iozxj8I&t=1022s)

36 more from 2021 on this thread: https://mlopstalks.com/threads/the-platform-team/2021

## 2022

31 sessions.

- [On Structuring an ML Platform 1 Pizza Team](https://mlopstalks.com/talks/on-structuring-an-ml-platform-1-pizza-team) (Breno Costa & Matheus Frata, Neoway). Pushed back: The platform team's size was described as four technical people earlier and as five people later in the discussion. [31:59](https://www.youtube.com/watch?v=66A72NgSfeE&t=1919s)
- [Platform Thinking: A Lemonade Case Study](https://mlopstalks.com/talks/platform-thinking-a-lemonade-case-study) (Orr Shilon, Lemonade). Pushed back: Lemonade's platform does not require one fixed level of openness: experienced users can customize decisions while other users can rely on defaults. [27:21](https://www.youtube.com/watch?v=KemCHs7Xbrs&t=1641s)
- [Feature Stores at Shopify and Skyscanner](https://mlopstalks.com/talks/feature-stores-at-shopify-and-skyscanner) (Matt Delacour, Shopify & Mike Moran, Skyscanner). Pushed back: Matt Delacour says Shopify's internal library is a thin layer on top of Feast, rather than a copied subset of Feast. [20:24](https://www.youtube.com/watch?v=v42YVgPGKro&t=1224s)
- [Lessons from Studying FAANG ML Systems](https://mlopstalks.com/talks/lessons-from-studying-faang-ml-systems) (Ernest Chan, Duo Security). Pushed back: Ernest Chan argued that teams should not simply copy the priorities of large companies because each platform reflects its own company’s requirements. [32:51](https://www.youtube.com/watch?v=Xqme2sr36RU&t=1971s)

27 more from 2022 on this thread: https://mlopstalks.com/threads/the-platform-team/2022

## 2023

19 sessions.

- [ML in Production: A DS from Ubisoft Perspective](https://mlopstalks.com/talks/ml-in-production-a-ds-from-ubisoft-perspective) (Jean-Michel Daignan, Ubisoft). Pushed back: Jean-Michel Daignan rejects the idea that data scientists should be required to adopt extensive unit and integration testing, emphasizing scalability testing instead. [14:54](https://www.youtube.com/watch?v=6mea_qHJLkw&t=894s)
- [Airflow Sucks for MLOps](https://mlopstalks.com/talks/airflow-sucks-for-mlops) (Stephen Bailey, Whatnot). Pushed back: Stephen Bailey argues that data teams should balance business value and platform quality rather than always building the most rigorous solution before releasing anything. [19:39](https://www.youtube.com/watch?v=C1dxY1tCmFo&t=1179s)
- [Machine Learning Education at Uber](https://mlopstalks.com/talks/machine-learning-education-at-uber) (Melissa Barr & Michael Mui, Uber). Pushed back: Melissa Barr said that a company without a homegrown machine learning platform may not need to build its own internal education program because strong external resources already exist. [42:26](https://www.youtube.com/watch?v=N6EbBUFVfO8&t=2546s)
- [LLM on Kubernetes](https://mlopstalks.com/talks/llm-on-kubernetes) (Shrinand Javadekar, Outerbounds & Manjot Pahwa, Lightspeed India & Rahul Parundekar, AI Hero & Patrick Barker). Pushed back: Rahul Parundekar argues that companies should prioritize a repeatable deployment platform and model iteration over over-optimizing Kubernetes autoscaling while GPUs are scarce. [16:42](https://www.youtube.com/watch?v=0e5q4zCBtBs&t=1002s)

15 more from 2023 on this thread: https://mlopstalks.com/threads/the-platform-team/2023

## 2024

29 sessions.

- [AI Innovations: The Power of Feature Platforms](https://mlopstalks.com/talks/ai-innovations-the-power-of-feature-platforms) (Mahesh Murag, Tecton & Jose Navarro, Cleo & Nikhil Garg, Fennel). Pushed back: Nikhil Garg says a feature store and a feature platform are different: a feature store mainly provides storage and serving, while a feature platform also includes computation and other end-to-end capabilities. [44:51](https://www.youtube.com/watch?v=xI_uQF072GE&t=2691s)
- [Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero](https://mlopstalks.com/talks/global-feature-store-optimizing-locally-and-scaling-globally-at-delivery-hero) (Gottam Sai Bharath & Cole Bailey, Delivery Hero). Pushed back: The participants disputed whether one centralized machine learning platform should define tooling and priorities for all departments. [4:03](https://www.youtube.com/watch?v=c9L8vAvCUyA&t=243s)
- [Reinvent Yourself and Be Curious](https://mlopstalks.com/talks/reinvent-yourself-and-be-curious) (Stefano Bosisio, Synthesia). Pushed back: Stefano Bosisio disputes the assumption that technically impressive ML platforms will be adopted automatically, arguing that internal communication and education are necessary. [18:48](https://www.youtube.com/watch?v=h-WGL-57OfU&t=1128s)
- [Building an ML Platform from scratch](https://mlopstalks.com/talks/building-an-ml-platform-from-scratch) (). Pushed back: Ben says Prefect is the one tool he believes is nearly always useful, while other platform tools can be overkill for a single person; Eric's discussion emphasizes starting with simpler monolithic pipelines. [1:37:43](https://www.youtube.com/watch?v=9LxsViICsJo&t=5863s)

25 more from 2024 on this thread: https://mlopstalks.com/threads/the-platform-team/2024

## 2025

14 sessions.

- [Re-Platforming Your Tech Stack](https://mlopstalks.com/talks/re-platforming-your-tech-stack) (Michelle Marie Conway & Andrew Baker, Lloyds Banking Group). Pushed back: The team's machine learning operations requirements differed from what a platform team focused mainly on model development would initially provide. [22:00](https://www.youtube.com/watch?v=1ouSuBETkdA&t=1320s)
- [Comparing ZenML, Metaflow, and all the other DAG tools](https://mlopstalks.com/talks/comparing-zenml-metaflow-and-all-the-other-dag-tools) (). Pushed back: Eric rejects the interpretation that the platform stack makes ZenML and Metaflow genuinely interchangeable without substantial work. [29:49](https://www.youtube.com/watch?v=W6hpEO80q20&t=1789s)
- [Iceberg, MCP, and MLOps: Bridging the Gaps for Enterprise](https://mlopstalks.com/talks/iceberg-mcp-and-mlops-bridging-the-gaps-for-enterprise) (Caleb Baechtold, Snowflake & Hamza Tahir, ZenML & Simba Khadder, Featureform). Pushed back: Simba Khadder and Caleb Baechtold explain Iceberg's stronger adoption than Delta by pointing to openness and weaker vendor lock-in, while noting Delta's Python support advantages. [57:40](https://www.youtube.com/watch?v=gicCH6FC-a4&t=3460s)
- [Hard Learned Lessons from Over a Decade in AI](https://mlopstalks.com/talks/hard-learned-lessons-from-over-a-decade-in-ai) (Mike Del Balso, Tecton). Pushed back: Mike Del Balso disputes the assumption that platform teams can reliably summarize their value by adding up dollar estimates from internal users. [32:26](https://www.youtube.com/watch?v=tgGjFmrmxE0&t=1946s)

10 more from 2025 on this thread: https://mlopstalks.com/threads/the-platform-team/2025

## 2026

7 sessions.

- [Accelerating Growth Through Optimizing GPU Usage](https://mlopstalks.com/talks/accelerating-growth-through-optimizing-gpu-usage) (Sahil Khanna, Adobe). Claim: Adobe's compute platform aims to improve developer productivity by simplifying access to GPU instances. [3:43](https://www.youtube.com/watch?v=SY9c92UfOSU&t=223s)
- [From Notebooks to Production FASTER](https://mlopstalks.com/talks/from-notebooks-to-production-faster) (Shahd Alghrsi, Virgin Media). Claim: The team serves about 200 data scientists and develops platform tools with their feedback. [2:01](https://www.youtube.com/watch?v=4JNvJwnXmcA&t=121s)
- [A Playground for AI Engineers](https://mlopstalks.com/talks/a-playground-for-ai-engineers) (Paulo Vasconcellos, Hotmart). Pushed back: Newly discussed tools such as TüN should not be adopted merely because the industry is discussing them; they should solve a real problem first. [40:30](https://www.youtube.com/watch?v=9x6h_HRZG7s&t=2430s)
- [How We Cut LLM Latency 70% With TensorRT in Production](https://mlopstalks.com/talks/how-we-cut-llm-latency-70-with-tensorrt-in-production) (Maher Hanafi, Betterworks). Pushed back: Maher Hanafi says senior engineers initially believed AI would not substantially change their work, but later adopted it heavily. [48:45](https://www.youtube.com/watch?v=wTrv1hMQbVg&t=2925s)

3 more from 2026 on this thread: https://mlopstalks.com/threads/the-platform-team/2026