2021-11-14, 12:30–13:00, Room 2
MLOps is becoming an essential tool for a ML project's lifecycle.
This session will present the challenges when building and maintaining a ML application
and how MLFlow can contribute e2e to a project's lifecycle.
MLOps has become one of the hottest topics in machine learning for production.
Developing a machine learning project not also adopts the traditional software engineering concepts but also embodies a pipeline of repetitive practical challenges such working with different datasets, keep track of parameters when training and tuning AI models, promoting and deploy models to production.
Open source communities have contributed to democratize MLOps. MLFlow is a powerful tool that offers a complete API, that leverages to design and implement a production grade machine learning application lifecycle.
Stavros Niafas has received his diploma in Computer Engineering from TEI of Central Greece. He also holds an MSc in Image Synthesis & Multimedia and an Msc in Data Science from NCSR Demokritos and the University of Peloponnese.
He works as Machine Learning engineer in Digital Market Intelligence while his research interests expand in domains of Machine/Deep learning, Computer Vision and data-centric AI. He is also actively engaged in systems engineering, FLOSS & photography.