Conway's Law suggests software architecture shapes the structures and dynamics of the organization that produced it. In this panel we will dive into the collaborative process of bringing AI from experimental POCs to real-world applications, involving data scientists, DevOps/MLOps engineers, frontend engineers, and product managers. This session explores challenges spanning infrastructure, model accuracy, user interface design, and business alignment, highlighting successful strategies and fostering interdisciplinary communication. Together, we will try to uncover insights that enhance AI implementation, resulting in technically sound solutions that align with user needs and business goals.
Uri Rosenberg
Uri Rosenberg is the Specialist Technical Manager of AI & ML services within enterprise support at Amazon Web Services (AWS) EMEA. Uri works to empower enterprise customers on all things ML: from underwater computer vision models that monitor fish to training models on satellite images in space; from optimizing costs to strategic discussions on deep learning and ethics. Uri brings his extensive experience to drive success of customers at all stages of ML adoption.
Before AWS, Uri led the ML projects at AT&T innovation center in Israel, working on deep learning models with extreme security and privacy constraints.
Uri is also an AWS certified Lead Machine learning subject matter expert and holds an MsC in Computer Science from Tel-Aviv Academic College, where his research focused on large scale deep learning models.
Lior Khermosh
Lior is passionate and experienced in ML, AI, DNNs and MLops.
Lior was co-founder and Chief Scientist of ParallelM, a leader ML-Ops company.
Prior to that he held the distinguished Fellow role at PMC Sierra and was on the founding team of Passave, a FTTH silicon company.
He holds MSEE & BSEE degrees from Tel Aviv university, both Cum Laude.
Nikhil Gulati
Jaya Kawale
Jaya Kawale is the head of Machine Learning at Tubi, a Fox Corporation content platform. Jaya´s team works on solving various ML problems for Tubi´s product, ranging from recommendations, content understanding and acquisition, ads ML, etc. Her team also work on the application of cutting edge machine learning technologies such as contextual bandits, deep learning, computer vision and NLP to improve user experience at Tubi.
