Production AI Podcast Summaries
Production AI on Yedapo: 4 summarized podcast and YouTube episodes. Each includes key takeaways, core concepts and notable quotes with timestamps.
I Went to the Biggest AI Infrastructure Conference
Tech With Tim
Jun 7, 2026
Deploying AI agents reliably in production is a major challenge due to complex orchestration of retries and state management. Temporal's durable execution platform abstracts these issues, enabling developers to build robust AI applications that recover seamlessly from failures. Its widespread adoption by companies like OpenAI underscores its criticality.
Key insight: OpenAI significantly increased its usage of Temporal by over 60% in the last year, demonstrating Temporal's crucial role in scaling AI infrastructure for major industry players.
3.0 Agentic AI Specialisation with AgentOps Bootcamp
Krish Naik
Jun 3, 2026
Krish Naik announces a new agentic AI specialization bootcamp focused on the critical gap between building simple AI prototypes and deploying secure, enterprise-ready applications. The curriculum emphasizes agent operations (AgentOps), security guardrails, and observability, addressing real-world risks like unauthorized database deletion in automated workflows.
Key insight: The course specifically addresses the industry-wide failure of AI agents in production, citing incidents where autonomous agents have deleted entire production databases in seconds, necessitating rigorous deployment guardrails and LLM gateways.
RAG Just Got Inverted. Here's The Stack That Replaces It.
The AI Automators
May 25, 2026
Redis has launched Iris, a new architecture designed to solve the 'runtime' bottleneck in AI agents. By prioritizing fresh, high-speed data synchronization over static pre-compiled knowledge layers, Iris enables agents to navigate rapidly changing operational data in production environments.
Key insight: Redis Iris uses change data capture (CDC) to mirror operational databases in real-time, allowing agents to query live data rather than stale, pre-computed artifacts.
66 - Scaling LLMOps | Avi Lumelsky (Oligo)
LangTalks
Apr 12, 2026
Deploying LLMs at massive scale requires moving beyond naive experimentation to deterministic, cost-optimized pipelines. Avi Lomilsky explains how to balance latency and expense using strategic context engineering, caching, and model selection.
Key insight: By utilizing prompt caching and cross-region inference, companies can bypass rate limits and significantly reduce costs for real-time cybersecurity detection at scale.