MLOps Coffee Sessions #91 with Joseph Haaga, The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence.
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// Abstract
Joseph Haaga and the Interos team walk us through their design decisions in building an internal data platform. Joseph talks about why their use case wasn't a fit for off-the-shelf solutions, what their internal tool Snitch does, and how they use git as a model registry.
Shipyard blogpost series: https://medium.com/interos-engineering.
// Bio
Joseph leads the ML Platform team at Interos, the operational resilience company. He was introduced to ML Ops while working as a Senior Data Engineer and has spent the past year building a platform for experimentation and serving. He lives in Washington, DC, with his dog Cheese.
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// Related Links
Website: https://joehaaga.xyz
Medium: https://medium.com/interos-engineering
Shipyard blogpost series: https://medium.com/interos-engineering
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Timestamps:
[00:00] Introduction to Joseph Haaga
[02:07] Please subscribe, follow, like, rate, and review our Spotify and YouTube channels
[02:31] New! Best of Slack Weekly Newsletter
[03:03] Interos [04:33] Global supply chain
[05:45] Machine Learning use cases of Interos
[06:17] Forecasting and optimization of routes
[07:14] Build, buy, open-source decision making
[10:06] Experiences with Kubeflow
[11:05] Creating standards and rules when creating the platform
[13:29] Snitches
[14:10] Inter-team discussions when processes fall apart
[16:56] Examples of the development process based on the feedback of ML engineers and data scientists
[20:35] Preserving flexibility when introducing new models and formats
[21:37] Organizational structure of Interos
[23:40] Surface area for product
[24:46] Use of Git Ops to manage boarding pass
[28:04] Cultural emphasis
[30:02] Naming conventions
[32:28] Benefit of a clean slate
[33:16] One-size-fits-all choice
[37:34] Wrap up
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