Rami Krispin demonstrates container-based deployment for an AI retrieval application, connecting reproducible development environments with the operational requirements of running AI services in production.
Rami Krispin
Data Science and Engineering Leader; Docker Captain · Docker community / LinkedIn Learning
4:00 PM UTC · 60 min
Use containers to make application dependencies portable and reproducible.
Trace a working application from local development toward production.
Consider scaling and environment differences when deploying AI workloads.
Rami Krispin demonstrates container-based deployment for an AI retrieval application, connecting reproducible development environments with the operational requirements of running AI services in production.
Data Science and Engineering Leader; Docker Captain · Docker community / LinkedIn Learning
Rami leads data and engineering work and teaches production-focused developer workflows as a Docker Captain and LinkedIn Learning instructor. His employer is not identified on this page.
Hosted by Aishwarya Srinivasan and Arvind Narayanamurthy.
AI Advisor / AI Educator
Aishwarya Srinivasan is the Co-Founder of The Gen Academy and a Developer Relations Lead at Nebius. Her experience spans Fireworks AI, Microsoft for Startups, Google Cloud and IBM, where she worked with developers and businesses on practical AI and machine-learning solutions. She holds a postgraduate degree in Data Science from Columbia University and teaches AI engineering, applied machine learning, generative AI and LLMOps.
Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade machine-learning solutions. He has built and deployed enterprise AI systems and focuses on translating AI concepts into practical, production-ready solutions. He holds a Master's from Carnegie Mellon University.