Started his career as a backend developer but early on specialized in Data Engineering. Mate has more than 15 years of experience in Data Engineering and supporting Data Scientists to build enterprise ML solutions. Mate is currently the CEO of DATAPAO, a data consultancy company specializing in distributed systems, working closely with Databricks and Microsoft. He worked as a Principle Instructor and Practice Lead at Databricks. Before that, he was Co-Founder and CTO at enbrite.ly, an award-winning startup specializing in digital fraud detection.
March 28, 2023 6:00 PM
Productionising Machine Learning models - MLOps from a practitioners point of view
On-demand
ENG
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Topic description
During the presentation, we will share learnings from multiple MLOps projects and show how we can efficiently build a reproducible, automated pipeline that follows the DevOps principles. We also will present common traps and risks with operationalizing ML projects while presenting an IoT use case.
The presentation answers the following questions:
- How can I productionise my ML models?
- How can I build an efficient development pipeline for ML models?
- How to build reproducible ML models?
- How to track code, data, and parameter lineage with ML models?
- What kind of deployment methods should I choose for my ML models?
- What are the risks and the caveats of implementing MLOps?
- How to apply ML models on edge? - an IoT use-case
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Summary
Building Machine Learning (ML) models, or AI models in popular culture, is increasingly common in all industries. As organizations progress from PoCs and the first projects with ML models that bring business value, they are faced with a big question: How can I put my model in production, how do I update it, and how do I close the gap between model development and production? In other words, how do I do MLOps?
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Presenter(s)
Mate Gulyas
CEO @DATAPAO
DATAPAO
Presenter(s)
Gulyás Máté
CEO @DATAPAO
DATAPAO
Started his career as a backend developer but early on specialized in Data Engineering. Mate has more than 15 years of experience in Data Engineering and supporting Data Scientists to build enterprise ML solutions. Mate is currently the CEO of DATAPAO, a data consultancy company specializing in distributed systems, working closely with Databricks and Microsoft. He worked as a Principle Instructor and Practice Lead at Databricks. Before that, he was Co-Founder and CTO at enbrite.ly, an award-winning startup specializing in digital fraud detection.
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