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Wed, April 22OpsTech DeepTech Architecture
More than 95 percent of GenAI pilots fail to reach production not because of capability, but because of missing engineering discipline. This session presents a practical blueprint for bridging that gap. Using two open-source templates refined through real-world enterprise deployments, you will learn how to build Model Context Protocol (MCP) servers and AI agents that are production-ready from day one.
Through detailed code walkthroughs and live demonstrations, you will explore FastAPI-based MCP server architecture, streaming agent implementations with PostgreSQL persistence, and observability with Langfuse tracing. The session also covers Kubernetes deployment patterns, rootless container configurations, SSO integration, session management, and automated recovery strategies. Attendees will leave with production-grade templates, deployment manifests, and concrete engineering patterns that transform prototypes into reliable enterprise systems.
What You Will Learn
Proven architectural patterns for deploying enterprise-grade MCP servers and AI agents
How to implement observability, authentication, and failure recovery from the start
Practical deployment techniques using Kubernetes, OpenShift, and containerized environments
Access to open-source templates with full documentation, ready for immediate use
Who Should Attend
AI engineers, software architects, DevOps specialists, and enterprise developers responsible for taking AI systems from proof of concept to production at scale.
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Tuhin Sharma is Senior Principal Data Scientist at Redhat in the Data & AI team. Prior to that, he worked at Hypersonix as an AI architect and at IBM Watson as Data Scientist. He also co-founded and has been CEO of Binaize (backed by Techstars), a website conversion intelligence product for e-commerce SMBs. He received a master's degree from IIT Roorkee and a bachelor's degree from IIEST Shibpur in Computer Science. He loves to code and collaborate on open-source projects. He is one of the top 20 contributors of pandas. He has 4 research papers and 5 patents in the fields of AI and NLP. He is a reviewer of the IEEE MASS conference, Springer nature and Packt publication in the AI track. He writes deep learning articles for O'Reilly in collaboration with the AWS MXNET team. He is a regular speaker at prominent AI conferences like O'Reilly Strata & AI, PyCon, PyData, ODSC, GIDS, Devconf, Datahack Summit etc.
