// Abstract

MLOps is too tool-driven, don't let FOMO drive you to pick the latest feature/model/evaluation/ store but pay closer attention to what you actually need to release more safely and reliably.

// Bio

Matthijs is a Machine Learning Engineer, active in Amsterdam, The Netherlands. His current work involves training MLEs at Xccelerated.io. This means Matthijs divides his time between building new training materials and exercises, giving live trainings, and acting as a sparring partner for the Xccelerators at their partner firms, as well as doing some consulting work on the side.

Matthijs spent a fair amount of time contributing to their open scientific computing ecosystem through various means. He maintains open source packages (scikit-lego, seers) as well as co-chairs the PyData Amsterdam conference and meetup.

// MLOps

Jobs board  https://mlops.pallet.xyz/jobs

// Related Links

https://www.youtube.com/watch?v=appLxcMLT9Y

https://www.youtube.com/watch?v=Z1Al4I4Os_A

--------------- ✌️Connect With Us ✌️ -------------

Join our slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/

Connect with Matthijs on LinkedIn: https://www.linkedin.com/in/mbrouns/

Timestamps:

[00:00] Introduction to Matthijs Brouns

[00:28] Takeaways

[03:09] Best of Slack Newsletter

[03:38] AI MLFlow

[04:43] Nanny ML

[05:08] Best confinement buy over the last 2 years

[06:35] Matthijs' day-to-day

[08:24] What's hot right now?  

[09:36] ML space, orchestration, deployment

[10:21] Scaling

[13:20] Low-risk releases

[15:27] Scale Limitations or Fundamental in API

[16:33] MLOps maturity to a certain point

[18:57] Interdisciplinary leverage need

[21:11] PyScript  

[22:41] Next pipeline tools  

[24:02] General pattern to build your own tools

[30:25] Technology recommendation to a chaotic space

[33:46] Structured data vs tabular data  

[35:52] Big barriers in production

[37:57] Standardization

[39:20] Automation tension between the engineering side and data science side

[41:50] Low-hanging fruit

[42:30] Human check

[43:43] Rapid fire questions

[48:30] PyData Meetups