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From zero to MLOps: An open source stack to fight spaghetti ML

180 minutes


The ecosystem of MLOps tools and platforms keeps growing by the year and it’s difficult to stay up to date. Luckily our industry is now more mature and certain good practices are already well established, but it’s still difficult for newcomers to navigate the complexity of production machine learning systems.

What are the minimal pieces that you need to build your MLOps stack? Is there a way to avoid vendor lock-in by stitching open source components together? What are the pros and cons of this approach? What have we learned since 2015, when the seminal Google paper “Hidden Technical Debt in Machine Learning Systems” appeared?

The speaker

Juan Luis Cano Rodríguez

Juan Luis Cano Rodríguez

Juan Luis (he/him/él) is an Aerospace Engineer with a passion for tech communities, outreach, and sustainability. He works at QuantumBlack, AI by McKinsey, as Product Manager for Kedro, an opinionated Python framework for creating reproducible, maintainable and modular data science code. He has worked as Developer Advocate at Read the Docs, as software engineer in the space, consulting, and banking industries, and as a Python trainer for several private and public entities.

Apart from being a long-time user and contributor to many projects in the scientific Python stack (NumPy, SciPy, Astropy) he has published several open-source packages, the most important one being poliastro, an open-source Python library for interactive astrodynamics used in academia and industry.

Finally, Juan Luis is the founder and former chair of the Python España association, the point of contact for the Spanish Python community, former organizer of PyCon Spain, which attracted 800 attendees in its last in-person edition in 2022, and current organizer of the PyData Madrid monthly meetups.