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How Hera is an Enabler of MLOps Integrations // Flaviu Vadan // Coffee Sessions #115
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MLOps Coffee Sessions #115 with Flaviu Vadan, Senior Software Engineer at Dyno Therapeutics, How Hera is an Enabler of MLOps Integrations co-hosted by Vishnu Rachakonda.
// Abstract
Flaviu talks about the internal ML platform at Dyno Therapeutics called Hera. His team uses Hera as an internal innovation engine to help discover new breakthroughs with machine learning in the biotech healthcare industry.
/ Bio
Flaviu is a Senior Software Engineer at Dyno Therapeutics, the leading organization in the design of novel gene therapy vectors with transformative delivery properties for a vast landscape of human diseases. Flaviu comes from a background focused on Bioinformatics, which is a field that combines Computer Science, Mathematics, and Biology. He took stints in academia by working as a research assistant in Computer Science and Bioinformatics labs before joining Dyno Therapeutics to work on machine-guided design of adeno-associated viruses (AAVs).
At Dyno, Flaviu works on compute and core infrastructure, DevOps, MLOps, and approaches that combine AI/ML to design AAVs in silico. He is also the author and maintainer of Hera, a Python SDK that facilitates access to Argo Workflows by making workflow construction and submission easy and accessible.
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// Related Links
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Flaviu on LinkedIn: https://www.linkedin.com/in/flaviuvadan/
Timestamps:
[00:00] Introduction to Flaviu Vadan
[00:50] Takeaways
[02:06] Share this episode with a friend!
[03:20] What Dyno does
[05:44] CRISPR and Gene Editing
[06:21] Kidney transplants and using pig organs
[07:31] Deciding what genes to put in the body
[07:48] Role of ML at Dyno
[10:07] Higher dose
[13:41] Process of Machine Learning Deployment and Productionizing at Dyno
[16:22] Proliferation of models
[17:31] Building the internal platform
[19:37] Interaction with data, translation to compute layer, evaluation
[24:21] Venn diagram for MLOps
[27:06] Leveraging Argo Workflows
[30:34] Hera
[35:28] Open sourcing
[38:44] Human power at Dyno
[41:17] Wrap up
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