Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations.
Reinvent Yourself and Be Curious // MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia.
// Abstract
This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump on a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform.
// Bio
Sai Bharath Gottam
Stefano Bosisio is an MLOps Engineer, with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods, to understand and unlock the drug discovery problem.
After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field.
Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting and running half-marathons.
// MLOps Jobs board
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// MLOps Swag/Merch
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// Related Links
Website: https://medium.com/@stefanobosisio1First MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/
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Connect with Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/
Timestamps:
[00:00] Stephano's preferred coffee
[00:12] Takeaways
[01:06] Stephano's MLOps Course
[01:47] From Academia to AI Industry
[09:10] Data science and platforms
[16:53] Persistent MLOps challenges
[21:23] Internal evangelization for success
[24:21] Adapt communication skills to diverse individual needs
[29:43] Key components of ML pipelines are essentia
l[33:47] Create a generalizable AI training pipeline with Kubeflow
[35:44] Consider cost-effective algorithms and deployment methods
[39:02] Agree with dream platform; LLMs require simple microservice
[42:48] Auto scaling: crucial, tricky, prone to issues
[46:28] Auto-scaling issues with Apache Beam data pipelines
[49:49] Guiding students through MLOps with practical experience
[53:16] Bulletproof Problem Solving: Decision trees for problem analysis
[55:03] Evaluate tools critically; appreciate educational opportunities
[57:01] Wrap up
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