Naman Kaley
Docker Captain | Docker Certified Associate | Hands-On Transformative AI Leader | Architect of Generative AI & Neuroscience-Inspired Systems | Solutions Architect
Jaipur, India
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Hi everyone, I’m Naman Kaley, a Docker Captain, Docker Certified Associate, and Software Engineer specializing in backend development, AI integration, and cloud infrastructure. I have experience working with LLMs, Kubernetes, and scalable microservices, building AI-driven solutions for real-world applications.
Passionate about automation, DevOps, and AI-powered workflows, I enjoy solving complex technical challenges, optimizing system performance, and leveraging containerization—especially with Docker—to enhance scalability and efficiency.
Area of Expertise
Topics
The Cascading Failure: How a Simple Ingress Change Broke Our Pod Budget and How We Recovered
It started as a routine deployment — a backend service on our AKS cluster, just another namespace, just another Ingress. But one TLS secret later, our pods were stuck, our PodDisruptionBudget was broken, and production was at risk.
In this talk, I’ll share a real-world debugging journey where a small change in a Kubernetes Ingress triggered cascading effects across namespaces and violated a PodDisruptionBudget. I’ll walk through how we diagnosed the issue, cordoned and uncordoned nodes to regain control, and leveraged replication factors for recovery.
Attendees will walk away understanding how PodDisruptionBudgets really behave under pressure, how node cordoning helps restore balance, and why even minor configuration changes can have major operational impacts.
Sandboxing Autonomous AI: Zero-Trust Agents with Docker Hardened Images
This high-velocity lightning talk demonstrates a zero-trust execution architecture for stateful AI workloads. By replacing standard Python base images with recently open-sourced, distroless Docker Hardened Images (DHI), the AI's execution environment is stripped of unnecessary binaries, shells, and package managers. This effectively neutralizes post-exploitation lateral movement.Attendees observe a live demonstration of a stateful LangGraph agent attempting to execute a malicious payload, only to be seamlessly blocked by the architectural constraints of a hardened base image. The session provides a practical blueprint for securing heuristic AI without sacrificing operational functionality.
Sandboxing Agentic Workflows: Securing MCP Integrations with Docker
As developers increasingly connect Large Language Models to enterprise tools, executing non-deterministic AI workflows introduces severe security vulnerabilities. Granting autonomous agents unfettered access to infrastructure via systems like the Model Context Protocol (MCP) creates unacceptable supply chain risks and potential state corruption if not strictly isolated.
This session demonstrates how to mitigate these risks by wrapping agentic workflows in ephemeral Docker and E2B sandboxes. Rather than trusting the AI model, engineers must trust the boundaries. The presentation will walk through a live, production-ready architecture where an LLM securely interacts with a GitHub MCP server entirely within an isolated container environment. >
Attendees will learn to architect zero-trust boundaries around AI agents, configure MCP gateways directly within Docker sandboxes, and safely automate external systems without exposing host infrastructure. By wrapping non-deterministic AI in highly deterministic infrastructure, teams can scale their AI-native transformations without compromising platform integrity.
Quality Gates and Observability in AI-Driven Systems
AI systems demand more than accuracy they demand reliability, traceability, and observability. In this session, I’ll walk through building strong quality gates and observability layers for AI-powered microservices running on Kubernetes. We’ll explore how to embed sanity checks directly in GitHub workflows, apply SOLID principles for maintainable pipelines, and use monitoring stacks like Prometheus and Grafana for microservice health.
Beyond standard observability, I’ll dive into AI-specific insights tracking token usage, latency, and response consistency for LLMs with tools like Langfuse and Arize Phoenix, ensuring operational transparency in AI workflows. The talk wraps up with how feature flags (LaunchDarkly) and kill switches can enable safer rollbacks and faster recovery from failure. Expect practical takeaways that bridge AI reliability with cloud-native observability best practices.
Platform Engineering in the Age of Generative AI: Architecture, Ops & Resilience
This session dives into the challenges of modern AI platform development in the age of LLMs. You’ll learn how to address platform scalability, cost pressures, and infrastructure redesign to support high-performance, reliable AI services.
Key Focus Areas
- Identifying platform bottlenecks unique to LLM / generative AI systems
- Addressing inefficiencies in cost, resource usage, and latency
- Introducing design paradigms and architectural patterns for resilient, scalable AI platforms
Takeaways
Participants will walk away with:
- Strategies to design platforms with better cost control and efficiency
- Insights into AI/ML ops tools and infrastructure patterns for sustainable innovation
- Concrete approaches to scale LLM deployments without compromising quality
- Methods to leverage LLMs themselves to optimize and fix pipeline issues
LangGraph: The Game-Changer in Heuristic Learning AI Agents and RAG Control
In the evolving landscape of AI, heuristic learning and Retrieval-Augmented Generation (RAG) are pushing the boundaries of intelligent agents. LangGraph revolutionizes this space by enabling structured, multi-step reasoning workflows with stateful AI agents.
This session will explore how LangGraph enhances AI agents by introducing graph-based control flows, improving decision-making, and optimizing RAG pipelines for more context-aware, reliable, and efficient responses.
Kubernetes on Autopilot: AI Agents for Monitoring, Scaling & Recovery
In this interactive webinar, we will explore how to deploy, scale, and self-heal applications on Kubernetes using AI-powered automation. We’ll start with the fundamentals of Kubernetes, including clusters, nodes, pods, services, and scaling strategies. Then, we’ll dive into an interactive dashboard that allows real-time Kubernetes management, such as increasing replicas, modifying service types, and monitoring resources.
Additionally, we’ll introduce LangGraph-based AI agents to enhance observability and fault recovery. These AI-driven agents will analyze logs, detect issues, and suggest automated remediation steps—taking DevOps efficiency to the next level.
Kubernetes on Autopilot: AI Agents for Monitoring, Scaling & Recovery
This session explores deploying, scaling, and self-healing applications on Kubernetes using AI automation. It covers Kubernetes fundamentals, real-time cluster management via an interactive dashboard, and AI-powered log analysis with LangGraph for automated troubleshooting. Attendees will learn to deploy applications on AWS EKS, manage auto-scaling, and implement AI-driven fault recovery. The session is designed to be beginner-friendly while providing valuable insights for experienced professionals.
Kubernetes on Autopilot: AI Agents for Monitoring, Scaling & Recovery
In this interactive webinar, we will explore how to deploy, scale, and self-heal applications on Kubernetes using AI-powered automation. We’ll start with the fundamentals of Kubernetes, including clusters, nodes, pods, services, and scaling strategies. Then, we’ll dive into an interactive dashboard that allows real-time Kubernetes management, such as increasing replicas, modifying service types, and monitoring resources.
Additionally, we’ll introduce LangGraph-based AI agents to enhance observability and fault recovery. These AI-driven agents will analyze logs, detect issues, and suggest automated remediation steps—taking DevOps efficiency to the next level. By the end of the session, attendees will have hands-on experience in:
✅ Deploying applications on AWS EKS using ECR
✅ Managing auto-scaling and pod distribution dynamically
✅ Implementing AI-driven monitoring & fault recovery
✅ Automating Kubernetes troubleshooting using LangGraph
This session is beginner-friendly yet insightful for experienced professionals, making Kubernetes automation easier and smarter than ever before!
ImagePullSecrets Are Not Enough: Optimal Registry Strategies for Multi-Cloud Kubernetes
Ever wondered why some Kubernetes deployments can pull images from ACR, ECR, or GHCR without any imagePullSecrets in the YAML? In one cluster you need a secret, in another it “just works”—and nobody knows why.
In this talk, I break down what’s really happening behind the scenes when Kubernetes pulls private images across different clouds. We’ll look at how AKS, EKS, and GKE use identities, default service accounts, and mutating webhooks to authenticate without you specifying anything. Then we’ll shift to the multi-cloud reality—where these cloud-specific tricks fall apart.
I’ll share practical patterns for building a clean, cloud-agnostic approach to image pulling: using centralized registries, syncing cloud-native registries, handling secrets at the namespace level, and avoiding hidden behaviors that cause surprises during deployments.
If you’ve ever been confused about how Kubernetes “magically” connects to your container registry—or you’re working across multiple clouds—this session will give you clarity, a mental model that actually makes sense, and a simple architecture you can apply right away.
Kubernetes on Autopilot: AI Agents for Monitoring, Scaling & Recovery
This session explores deploying, scaling, and self-healing applications on Kubernetes using AI automation. It covers Kubernetes fundamentals, real-time cluster management via an interactive dashboard, and AI-powered log analysis with LangGraph for automated troubleshooting. Attendees will learn to deploy applications on AWS EKS, manage auto-scaling, and implement AI-driven fault recovery. The session is designed to be beginner-friendly while providing valuable insights for experienced professionals.
Native Authentication Patterns for Azure Kubernetes Service
Hardcoded credentials in YAML are a security risk we all know about, but plenty of teams still use manual imagePullSecrets or long-lived service principals to pull from private registries. It's a pain to rotate, clutters your manifests, and expands your attack surface.
In this lightning talk, I'll show you how to ditch the secrets completely. We'll use Azure Managed Identities to let AKS authenticate directly with Azure Container Registry.
Here's what we'll cover:
The Architecture: How the Kubelet identity authenticates with ACR without any stored credentials.
The Implementation: The Terraform/CLI commands you need to attach identities (it's like 3 commands).
The Result: Your cluster pulls private images securely. No base64 blobs, no secrets to rotate, no problem.
Bridging the Auth Gap: Legacy to Keycloak on AKS
This lightning talk introduces a high-velocity architectural pattern for modernising legacy identity without rewriting underlying application code. The presentation details how to deploy Keycloak as an identity broker directly at the AKS cluster edge. By leveraging native Kubernetes ingress controllers to intercept unauthenticated requests, Keycloak dynamically wraps outdated login mechanisms into standardised OIDC protocols.
Attendees acquire a rapid-deployment strategy to secure vulnerable monolithic endpoints instantly. The session provides a definitive blueprint for bridging the identity gap, allowing infrastructure teams to enforce zero-trust policies across hybrid AKS workloads while long-term application modernisation continues seamlessly in the background.
Azure Node Management: Surviving a PDB Deadlock During Routine Upgrades
It was supposed to be a standard Tuesday. We were rotating a TLS secret for an Ingress in our elastic namespace on AKS routine stuff. But within minutes, what should've been a quick config change turned into a complete deadlock.
Our Pod Disruption Budgets did exactly what they were designed to do: block evictions. The problem? They worked too well. They froze the upgrade path, trapped our pods, and prevented the Azure nodes from scaling down. The cluster couldn't heal itself, and we were stuck.
In this session, I'll walk through what went wrong and how we manually recovered the namespace without losing data. We'll go beyond basic kubectl commands and cover:
The Trap: How a mismatch between PDBs and deployment rolling updates creates a dependency loop that locks everything up.
The Fix: The actual cordon, drain, and uncordon steps we used on AKS nodes to force a reset. I'll show you what worked (and what didn't).
The Lesson: How to calculate PDB values that won't break your cluster during Azure's upgrade cycles so you don't end up troubleshooting at 3am like we did.
Squeezing the Monolith: Transforming Legacy Apps into Containers
In this session, we’ll explore the practical steps and strategies for migrating a monolithic application into a Docker container. From breaking down dependencies and managing database connections to optimizing performance and scaling, we'll cover the key challenges and solutions to successfully containerizing legacy systems. Learn how to retain the robustness of your monolith while leveraging the flexibility, portability, and scalability of containers. Whether you’re just starting your container journey or looking to refine your deployment strategies, this session will provide actionable insights to help you modernize and future-proof your applications.
Harnessing Elasticsearch and LLMs for Next-Gen AI Solutions
This session will provide pathway that explores the intersection of Elasticsearch as a vector data store and the transformative potential of large language models (LLMs). We will delve into how Elasticsearch can efficiently manage and query vector embeddings, providing a powerful foundation for modern AI applications. Learn practical strategies for integrating LLMs to build, enhance, and deploy innovative AI solutions that push the boundaries of what's possible in machine learning and data intelligence. Whether you're aiming to create robust search functionalities or sophisticated AI-driven tools, this session will equip you with the knowledge to design, develop, and implement cutting-edge AI architectures seamlessly.
LangGraph: The Game-Changer in Heuristic Learning AI Agents and RAG Control
In the evolving landscape of AI, heuristic learning and Retrieval-Augmented Generation (RAG) are pushing the boundaries of intelligent agents. LangGraph revolutionizes this space by enabling structured, multi-step reasoning workflows with stateful AI agents.
This session will explore how LangGraph enhances AI agents by introducing graph-based control flows, improving decision-making, and optimizing RAG pipelines for more context-aware, reliable, and efficient responses.
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