In this episode of AI Explained, we are joined by Gary Stafford, Principal Solutions Architect at AWS Strands Agents.

He delves into how enterprises choose between AI/ML and agentic approaches, patterns for multi-agent systems, and the role of MCP. Gary also shares real-world use cases and practical guidance on safety, scaling, and delivering enterprise-ready agent systems.

In this episode of AI Explained, we are joined by Jayeeta Putatunda, Director of AI Center of Excellence at Fitch Group. 

She discusses essential lessons learned from building and deploying AI agent systems, including challenges in moving from concept to production, key evaluation metrics, and the importance of observability and guardrails in ensuring reliable AI systems.

In this episode of AI Explained, we are joined by Nate B. Jones, AI strategist.

He explores high-level advice for organizations, technical ideas such as prompting and application architecture, and the current state of agent adoption. Key topics include challenges in building production-ready agents, architectural decisions, and ensuring ROI from these agents.

In this episode of AI Explained, we are joined by Karthik Bharathy, General Manager, AI Ops & Governance for Amazon SageMaker AI at AWS. 

He discusses the critical aspects of AI security and observability for agentic workflows. He covers the evolution of AI Ops, end-to-end observability, human oversight, the current state of AI in enterprises, and the ways agentic AI systems are transforming business operations. He also dives into the challenges of implementing AI security, evaluating AI decisions, and ensuring transparency and compliance.

In this episode of AI Explained, Dr. Girish Nadkarni from Icahn School of Medicine at Mount Sinai. 

He discusses the implementation and impact of AI, specifically generative AI, in healthcare. He covers topics such as clinical implementation, risk prediction, the interplay between predictive and generative AI, the importance of governance and ethical considerations in AI deployment, and the future of personalized medicine.

In this episode of AI Explained, we are joined by Navrina Singh, Founder and CEO at Credo AI.

We will discuss the comprehensive need for AI governance beyond regulated industries, the core principles of responsible AI, and the importance of AI governance in accelerating business innovation. The conversation also covers the challenges companies face when implementing responsible AI practices and dives into the latest regulations like the EU AI Act and state-specific laws in the U.S. 

In this episode of AI Explained, we are joined by Jonathan Cohen, VP of Applied Research at NVIDIA. 

We will explore the intricacies of NVIDIA's NeMo platform and its components like NeMo Guardrails and NIMS. Jonathan explains how these tools help in deploying and managing AI models with a focus on observability, security, and efficiency. They also explore topics such as the evolving role of AI agents, the importance of guardrails in maintaining responsible AI, and real-world examples of successful AI deployments in enterprises like Amdocs. Listeners will gain insights into NVIDIA's AI strategy and the practical aspects of deploying large language models in various industries.

On this episode, we’re joined by Kevin Schawinski, CEO and Co-Founder at Modulos AG

The EU AI Act was passed to redefine the landscape for AI development and deployment in Europe. But what does it really mean for enterprises, AI innovators, and industry leaders? 

Schawinski will share actionable insights to help organizations stay ahead of the EU AI Act, and discuss risk implications to meeting transparency requirements, while advancing responsible AI practices.

In this episode, we’re joined by Robert Nishihara, Co-founder and CEO at Anyscale.

Enterprises are harnessing the full potential of GenAI across various facets of their operations for enhancing productivity, driving innovation, and gaining a competitive edge. However, scaling production GenAI deployments can be challenging due to the need for evolving AI infrastructure, approaches, and processes that can support advanced GenAI use cases.

Nishihara will discuss reliability challenges, building the right AI infrastructure, and implementing the latest practices in productionizing GenAI at scale.

In this episode, we’re joined by Pradeep Javangula, Chief AI Officer at RagaAI

Deploying LLM applications for real-world use cases requires a comprehensive workflow to ensure LLM applications generate high-quality and accurate content. Testing, fixing issues, and measuring impact are critical steps of the workflow to help LLM applications deliver value. 

Pradeep Javangula, Chief AI Officer at RagaAI will discuss strategies and practical approaches organizations can follow to maintain high performing, correct, and safe LLM applications.