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Speaker

Alexander Chernov

Alexander Chernov

🤖 Link-Think-Act · Associate Principal Data Engineer @ AstraZeneca · Agentic Datasets & Intelligent MES · AgentOps · Cloud-Native / Hybrid Architectures · M.Sc. Physics - ICT

Toronto, Canada

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As a Subject Matter Expert, I operate at the intersection of agentic systems, large-scale data platforms, and real-time infrastructure, leading research and architecture for systems where data, equipment, and applications become active, self-governing entities.

My work focuses on defining and evolving agent-native system architectures that span cloud, on-prem, and hybrid environments. I design platforms where datasets, filesystems, equipment catalogs, and services own their workflows, lifecycle, and operational intent, enabling near real-time adaptation, autonomous coordination, and policy-driven execution.

I stay hands-on with the systems I design. I prototype, wire up, stress-test, and iterate on real platforms - often moving from whiteboard to running code, from UI interaction to control plane logic, from local experiments to production-grade infrastructure.

I work across frontend, backend, and infrastructure layers, not as isolated components but as a single system that must behave predictably under load, failure, and change. My focus is on making complex systems understandable, operable, and pleasant to evolve - for both humans and machines.

I build systems around reliability, security, and isolation by design, using declarative resource models, infrastructure-as-code, and custom controllers to map desired state into observable, governable reality. I actively integrate practices from AIOps, MLOps, DevSecOps, and Site Reliability Engineering, ensuring that intelligent systems remain explainable, operable, and safe at scale.

A core strength of my work is designing near real-time telemetry, metrics, and event pipelines that operate across heterogeneous environments and process data at multi-terabyte scale. These pipelines are not treated as observability add-ons, but as first-class architectural primitives that enable feedback, learning, and adaptation.

My experience spans high-reliability and regulated domains, including finance, pharmaceutical manufacturing, and industrial automation. This includes architecting reactive financial systems, contributing to GxP-aligned MES platforms, and designing control and automation systems for semiconductor manufacturing and robotics, with deep experience in SECS/HSMS-based integrations.

Area of Expertise

  • Finance & Banking
  • Information & Communications Technology
  • Manufacturing & Industrial Materials
  • Physical & Life Sciences
  • Region & Country

Topics

  • Kubernetes
  • Cloud Native & Kubernetes
  • Azure Kubernetes Services (AKS)
  • Kubernetes Security
  • Container and Kubernetes security
  • Google Kubernetes Engine
  • Kubernetes Operators
  • kubernetes autoscaling
  • enterprise kubernetes
  • Storage Kubernetes
  • Kubernetes Deployments
  • aws
  • AWS Architect
  • AWS S3
  • AWS Data
  • AWS Serverless
  • AWS Databases
  • AWS Lamda
  • AWS DevOps
  • AWS IoT

From Pipelines to Agents: Building Intelligent, Self-Managing Data Products on Kubernetes

As AI and automation reshape modern software, datasets themselves can become active, intelligent components in cloud-native systems. This session introduces Agentic Datasets - a Kubernetes-native pattern where datasets act as self-managing data products capable of reasoning, triggering workflows, and coordinating with AI models.

Each dataset runs as a DatasetAgent, defined as a Kubernetes Custom Resource (CRD) encapsulating metadata, logic, and declarative workflows packaged as OCI bundles. These workflows execute securely within the cluster as Pods or Jobs, performing automated tasks such as validation, reindexing, or embedding generation - without external orchestration.

Attendees will learn how controllers, service meshes, and GitOps pipelines enable these agents to operate autonomously, and how LLM-powered reasoning enhances workflow decision-making. The talk concludes with a reference architecture and practical steps for deploying intelligent, AI-assisted data products on Kubernetes using open-source tools.

Agentic Datasets on Kubernetes: Data Products with Active Workflows

This demo presents Agentic Datasets, a cloud-native pattern where datasets act as intelligent, self-managing data products on Kubernetes. Each dataset runs as a DatasetAgent Custom Resource encapsulating metadata, reasoning logic, and executable workflows defined through a lightweight SDK. Workflows are distributed as signed OCI bundles and executed securely within the cluster, enabling automated validation, embedding generation, and lineage tracking without external orchestration.
Using controllers and GitOps pipelines, Agentic Datasets integrate seamlessly with service mesh, observability, and governance frameworks. The poster illustrates how datasets can reason about their own content, apply LLM-assisted automation for summarization or decision tasks, and participate as active, autonomous workloads in AI-enabled Kubernetes environments.

Agentic Datasets on AKS: Data Products with Active Workflows

This session presents Agentic Datasets, a cloud-native pattern where datasets act as intelligent, self-managing data products on AKS. Each dataset runs as a DatasetAgent Custom Resource encapsulating metadata, reasoning logic, and executable workflows defined through a lightweight SDK. Workflows are distributed as signed OCI bundles and executed securely within the cluster, enabling automated validation, embedding generation, and lineage tracking without external orchestration.
Using controllers and GitOps pipelines, Agentic Datasets integrate seamlessly with service mesh, observability, and governance frameworks. The poster illustrates how datasets can reason about their own content, apply LLM-assisted automation for summarization or decision tasks, and participate as active, autonomous workloads in AI-enabled Kubernetes environments.
The design integrates with Azure OpenAI Service and Azure AI Foundry for LLM-assisted reasoning, enabling DatasetAgents to perform contextual analysis, summarization, and autonomous decision tasks directly from within Azure-hosted workflows.

Optimized AI Conference 2026 Sessionize Event Upcoming

March 2026 Atlanta, Georgia, United States

CNCF Toronto: 2026 Call for Speakers User group Sessionize Event Upcoming

February 2026 Toronto, Canada

Alexander Chernov

🤖 Link-Think-Act · Associate Principal Data Engineer @ AstraZeneca · Agentic Datasets & Intelligent MES · AgentOps · Cloud-Native / Hybrid Architectures · M.Sc. Physics - ICT

Toronto, Canada

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