Session Outline

You will learn how to get started in the journey of MLOps with practical examples for automating the ML pipeline. 

Key Takeaways

  • Relevance of MLOps as the only way to move AI into production
  • Risks of lack of MLOps implementation
  • Learn about Google Maturity levels
  • Practical example on hohw to get started

Speaker Bio

Oswaldo Gomez – Senior IT MLOps Engineer | Roche
Studied Physics at UNAM with a focus on Computational Physics and MSc in Big Data Science at UCA. I am an experienced professional currently working as a Senior IT Professional – MLOps Engineer at Roche in Poland. I’m passionate about the intersection between computing infrastructure and artificial intelligence. With experience in the IT, Financial and Pharmaceutical industries I have a wide spectrum of the different challenges that need to be taken into account when exposing ML in production. I’m totally inspired by my current role since I can contribute back to society in a more direct manner by helping accelerate drug discovery from my MLOPs engineer trench. I work with Kubernetes, Kubeflow, DKube, Python, AWS, ArgoCD, Gitlab and constantly looking for new cloud-native technologies that can help us bridge the gap to production while minimizing maintenance. Part of a novel team of MLOPs engineers at Roche with a common passion and a clear goal.

October 15 @ 13:30
13:30 — 13:50 (20′)

Day 2 | M3 | Data Engineering Stage

Oswaldo Gomez – Senior IT MLOps Engineer | Roche