Amar Deep Singh
GM Financial | AVP IT Architecture
Dallas, Texas, United States
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Amar Deep Singh is a distinguished software architect and author with extensive experience in microservices and cloud computing. He is the author of "Building and Delivering Microservices on AWS," a comprehensive guide that explores software architecture patterns and the deployment of microservices using AWS services.
Amar has more than 17 years of experience working for large firm like GM Financial,US Bank, JPMorgan Chase, Choice Hotel, Dell in different roles.
Area of Expertise
Topics
Operating MCP in the Enterprise: From Protocol to Production
Model Context Protocol (MCP) enables standardized AI agents, but real adoption depends on how MCP servers operate in production—not just how the protocol is defined.
This session provides a high-level, practitioner perspective on running MCP in enterprise environments. It covers how MCP servers fit into existing platforms, with a focus on observability, security, distributed tracking of agent behavior, and resiliency. Rather than diving deep into implementation details, the talk shares lessons learned from real deployments and common pitfalls teams face when moving MCP from experimentation to production.
Scaling API Governance: Role of AI/ML
In this session, we will explore the essential aspects of API governance, including its significance in maintaining architectural integrity and enabling the reusability of APIs. We will delve into the common challenges organizations face when implementing governance frameworks, such as ensuring adherence to standards, managing diverse stakeholders, and addressing compliance requirements.
To meet the demands of scaling API governance, we will discuss how AI/ML can transform traditional approaches. By leveraging AI-driven insights, organizations can automate compliance checks, enhance policy enforcement, and facilitate real-time monitoring, thereby empowering teams to achieve centralized governance without compromising agility.
Key Takeaways:
Understanding the critical need for API governance in modern architectures.
Identifying common challenges and pitfalls in governance implementation.
Learning how AI/ML can be harnessed to empower and scale governance efforts.
Exploring best practices for centralized governance to maximize API reusability
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