GenAIOps, MLOps, Agentic AI & Python Architecture from a Specialist Who Ships Production AI Systems
A common gap in enterprise AI adoption is the divide between theoretical prompt engineering and production infrastructure. Steven Nagendran (Red Hat Certified Developer in AI, RHCA, RHCI, MCT) actively designs, codes, and operates four live production platforms: MISS2ASIA (AI-powered 9-channel social media content generation & automated scheduling engine), Cognitoz AI (Enterprise GenAI & RAG platform on AKS), ixvena (AI personal health OS & connected wearable telemetry), and Cognitoz BetaLab (custom training sandbox platform).
Steven's GenAIOps, MLOps & Python specialization bridges software engineering, data science, and Kubernetes platform engineering. For infrastructure and MLOps teams, Steven consults on and teaches Kubeflow Pipelines on Kubernetes (LFS147), Red Hat OpenShift AI (AI067 — KServe, Ray, vLLM, NVIDIA GPU Operator), Operationalizing GenAI & LLMOps on Azure (AI-300), and Azure Databricks GenAI (DP-3028).
For software engineers, architects, and automation leads, Steven delivers intensive hands-on training and architectural advisory in Python for Enterprise Systems & Automation (PY-ENT), PyTorch Deep Learning (LFD273), Anthropic Claude Academy & Model Context Protocol (CLAUDE-101, CLAUDE-MCP, CLAUDE-API), Microsoft Agentic AI Architecture & Copilot Studio (AB-100, AB-620, AI-102, AI-103, AI-500), OpenAI Developer Platform, and developer-first agentic coding workflows (Claude Code, Google Antigravity, OpenAI Codex, Hermes Agent).
GenAIOps, LLMOps & MLOps on Kubernetes
Deploying, serving, scaling, and monitoring private open-weights LLMs and machine learning pipelines on Kubernetes and cloud.
- MLOps on Kubernetes with Kubeflow (LFS147): Kubeflow Pipelines (KFP), Katib hyperparameter tuning, JupyterHub workbenches, and model registries
- Private Hybrid AI with Red Hat OpenShift AI (AI067): NVIDIA GPU Operator, KServe / vLLM high-throughput model serving, and distributed Ray
- Operationalizing Generative AI & LLMOps (AI-300 / DP-3028): Prompt Flow pipelines, MLflow LLM evaluation benchmarks, model drift telemetry, and Mosaic AI Gateway on Databricks
Enterprise Python, PyTorch & RAG Engineering
Production-grade Python programming for infrastructure automation, REST/MCP APIs, deep learning, and Retrieval-Augmented Generation.
- Python for Enterprise Systems & Automation (PY-ENT): clean architecture, httpx/REST APIs, subprocess orchestration, Poetry/uv environments, and data pipelines
- PyTorch Essentials (LFD273) & Data Science: tensor CUDA acceleration, neural network modules (nn.Module), and Hugging Face transformer fine-tuning
- Enterprise RAG Architecture (proven on Cognitoz AI): document chunking, hybrid vector + BM25 search (Azure AI Search, pgvector, Qdrant), and hallucination guardrails
Multi-Agent Systems, Model Context Protocol (MCP) & Copilot Studio
Architecting autonomous AI agents that securely connect to enterprise databases, APIs, and internal workflows.
- Microsoft Agentic AI Architecture (AB-100 / AI-103 / AI-500) & Copilot Studio (AB-620): multi-agent orchestration, Azure AI Foundry, Semantic Kernel, AutoGen, and Agent2Agent (A2A) protocols
- Anthropic Claude Academy (CLAUDE-101 / CLAUDE-MCP / CLAUDE-API): building custom Model Context Protocol (MCP) servers in Python/TypeScript to connect LLMs to internal databases and Git
- OpenAI Academy (OAI-AGENT / OAI-DEV) & Google Gemini Cloud (GCP-GEMINI): Structured Outputs, Assistants API, tool calling, and Vertex AI
AI Security Governance, Executive Strategy & Agentic Coding
Securing the AI agent workforce against prompt injection and shadow AI while accelerating engineering teams with agentic IDEs.
- AI Security & Governance (AB-6007 / AB-900 / AB-650 / SC-5009): OWASP Top 10 for LLMs, prompt injection defenses, Entra Agent IDs, Defender for Cloud AI posture, and Purview DLP
- Executive & Workforce AI Transformation (AB-731 / AB-730 / AB-6008 / AI-3025): high-ROI workflow delegation and M365 Copilot mastery
- Developer-First Agentic Coding Bootcamps (GH-300 + Custom): mastering GitHub Copilot, Claude Code, Google Antigravity, OpenAI Codex, and Hermes Agent in real repositories
Real Production AI Case Studies: MISS2ASIA, Cognitoz AI, ixvena & BetaLab
Custom-built Python, MLOps, and Agentic AI / MCP bootcamps run on Cognitoz BetaLab (built by Steven), while official Microsoft & Red Hat AI courses run on official vendor labs via Authorized Training Partners.
- MISS2ASIA (miss2asia.com): Study the real architecture of Steven's MarkOps Intelligence Strategy System 2 — a multi-tenant AI social media content generation and 9-channel automated scheduling system powered by Memory Map Learning (MML) and Brand Intelligence (BI).
- Cognitoz AI (cognitoz.ai) & ixvena (ixvena.com): Explore production Retrieval-Augmented Generation (RAG) on Azure Kubernetes Service (AKS) and adaptive AI wearable health telemetry microservices.
- Cognitoz BetaLab (lab.cognitoz.com): Used exclusively for Steven's Custom-Built Python (PY-ENT), Kubeflow MLOps, and custom Agentic AI / Claude MCP coding bootcamps.
Production Deployments & Migration Track Record
Steven brings real architectural lessons from these enterprise deployments directly into every consulting engagement and hands-on classroom.
MISS2ASIA A.I Engine — MISS2ASIA: AI-Powered Automated Social Media Content Engine
MISS2ASIA is an AI-driven platform designed to generate high-engagement social media content—including captions, hashtags, images, and videos—for Facebook, Instagram, LinkedIn, TikTok, X, and YouTube. This project encompassed the end-to-end design and implementation of a multi-tenant web application.
Cognitoz AI — Implementation of Generative A.I based on OpenAI
Cognitoz AI is backed by Azure Kubernetes Service and Azure DevOps, the Project involves of implementing and maintaining the backend services for the Web App.
Ixvena Health App — Implementation of ixvene Health mobile/web app based on A.I
ixvena Health app is backed by A.I that was build and customized to cater Health related and Weight management, the Project involves of implementing and maintaining the backend services for the Web App and Mobile App, UI/UX Design, entire Microservice Architecture of the App.
Cognitoz BetaLab — Implementation of BetaLab Hands-On Training Lab Platform
BetaLab is Cognitoz's hands-on training platform that gives every student an isolated, ready-to-use lab environment in seconds. The project covers the end-to-end design and build of the student portal, admin control plane and backend, with live labs running on Kubernetes and lab infrastructure provisioned through automation.
Courses & Curricula Taught by Steven (41 Tracks)
Official vendor courses are delivered through Authorized Training Partners on official vendor labs; custom-built tracks run on Cognitoz BetaLab.
Introduction to Microsoft Azure Data
Foundations of relational and non-relational data workloads in Azure, cloud database services, and modern analytical reporting solutions.
Implement scalable database solutions using Azure SQL
Administering relational database solutions on Azure SQL Database and Azure SQL Managed Instance: performance tuning, automated backup, security, and HA.
Implement analytics solutions using Microsoft Fabric
End-to-end analytics engineering with Microsoft Fabric: Lakehouses, Data Warehouses, Apache Spark processing, Delta tables, semantic models, and Direct Lake Power BI.
Implement data engineering solutions using Microsoft Fabric
Designing and operationalizing enterprise data pipelines, Medallion architecture (Bronze/Silver/Gold), Spark transformations, and real-time streaming in Microsoft Fabric.
Designing and implementing a data science solution on Azure
Operationalizing machine learning models with Azure Machine Learning Studio, automated ML, MLflow model tracking, and managed endpoint deployment.
Data Engineering on Microsoft Azure
Traditional Azure data platform engineering with Azure Synapse Analytics, Azure Data Factory pipelines, and Azure Databricks Spark clusters.
Introduction to AI in Azure
Core Artificial Intelligence concepts: Computer Vision, Natural Language Processing, Conversational AI, and Generative AI on Azure cloud.
Develop AI solutions in Azure
Building production cognitive AI applications using Azure AI Services: Azure OpenAI, Document Intelligence, Azure AI Search, Speech, and Language understanding.
Get started with AI applications and agents on Azure
Architecting autonomous AI applications and agentic workflows using Azure AI Foundry, Semantic Kernel, and AutoGen agent orchestration.
Extend Microsoft Power Platform Solutions with code and AI
Pro-developer extensibility for Power Platform: creating Power Apps Component Framework (PCF) controls, custom plugins, Azure OpenAI / Azure AI Foundry API integration, and enterprise CI/CD deployment pipelines.
Build intelligent applications
Architecting agent-first business applications on Microsoft Power Platform: Dataverse modeling, AI Builder integration, automated agent workflows, and embedding Copilot capabilities into custom apps.
Introduction to Microsoft 365 and AI administration
Foundational administration for modern AI-driven workplaces: managing Microsoft 365 Copilot, deploying pre-built agents, configuring Entra ID identity baselines, Purview data hygiene, and security posture.
Administer Microsoft 365 and AI services
Enterprise-grade administration of Microsoft 365 and AI services: Copilot deployment policies, agent access control, Microsoft Purview sensitivity labeling, data loss prevention (DLP), and compliance telemetry.
GitHub Copilot for Enterprise Developers
Hands-on engineering workshop maximizing developer productivity with GitHub Copilot, Copilot Chat, context prompting, unit test generation, and secure coding practices.
Develop AI apps and agents on Azure
Building autonomous agents with Azure AI Foundry: tool integration, memory persistence, planning agents, and safety evaluation frameworks.
Operationalize machine learning and generative AI solutions
Deploying and monitoring LLMs in production: prompt flow pipelines, fine-tuning benchmarks, model drift detection, and enterprise scaling.
Design and implement multi-agent AI solutions
Architecting collaborative multi-agent swarms: message passing, distributed task delegation, consensus algorithms, and multi-model routing.
Architecting agentic AI business solutions
Master enterprise agentic AI architectures: multi-agent orchestration, Copilot Studio, Azure AI Foundry, Model Context Protocol (MCP), Agent2Agent (A2A) integration, and enterprise governance.
Design and build integrated AI agent solutions in Copilot Studio
End-to-end autonomous agent development in Microsoft Copilot Studio: generative actions, grounding on enterprise knowledge, Model Context Protocol (MCP) integrations, custom connectors, and Power Automate flows.
Work smarter with AI
Hands-on corporate workshop mastering Microsoft Copilot across daily workflows: prompting strategies, document synthesis, meeting action items, and data analysis.
Develop AI agents on Azure
Fast-paced developer workshop building goal-oriented autonomous AI agents using Azure AI Foundry SDK, tool connectors, and structured outputs.
Extract insights from visual data on Azure
Hands-on multimodal workshop extracting text, tables, and spatial entities from unstructured documents and images using Azure Document Intelligence and GPT-4o Vision.
Implement Generative AI engineering with Azure Databricks
Hands-on generative AI engineering on Azure Databricks: vector search indexing, MLflow evaluation, RAG pipelines, and model serving.
Introduction to building AI agents
Accelerated workshop creating intelligent conversational agents with Copilot Studio, generative answers, enterprise data sources, and custom topics.
Transform Ideas into Action with Copilot Chat (Basic)
Empowering business teams with Microsoft 365 Copilot Chat: iterative prompting, cross-document analysis, presentation drafting, and communication polish.
Secure AI solutions in the cloud using Microsoft Defender for Cloud and Microsoft Entra
Securing modern generative AI workloads: threat modeling for LLMs, prompt injection defenses, Entra ID workload identities, and Defender for Cloud AI posture.
Drive AI transformation in your organization
Executive masterclass guiding strategic AI roadmaps, ROI measurement, change management, ethical AI policies, and workforce upskilling.
Transform business workflows with generative AI
Hands-on generative AI masterclass for business productivity: multi-turn prompting, context engineering, drafting, financial data analysis in Excel, presentation synthesis in PowerPoint, and workflow streamlining via Microsoft 365 Copilot.
Secure and Govern your AI agent workforce
Governing autonomous AI agents across the enterprise: agent identity lifecycle, credential boundaries, preventing shadow agents, Microsoft Defender for Cloud posture, and Purview audit logging.
Delegate work with AI
Mastering task delegation to AI agents: decomposing workflows, assigning multi-step instructions, supervising autonomous agent outputs, and integrating automated handoffs across workplace applications.
Red Hat OpenShift AI Technical Overview
Private hybrid AI on Kubernetes with Red Hat OpenShift AI: managed Jupyter notebooks, Ray distributed training, KServe model serving, and GPU acceleration.
Python for Enterprise Systems & Automation
Writing clean, production-grade Python: data structures, REST API clients (requests/httpx), file I/O, regex, subprocess automation, and virtual environments.
Introduction to AI/ML Toolkits with Kubeflow
Orchestrating end-to-end machine learning on Kubernetes: Kubeflow Pipelines (KFP), Katib hyperparameter tuning, and containerized model training workflows.
PyTorch Essentials: An Applications-First Approach
Deep learning fundamentals using PyTorch: tensor computations, autograd automatic differentiation, convolutional networks, and Hugging Face transformer fine-tuning.
Claude 101: Foundation Principles & Prompt Engineering
Mastering Anthropic Claude: XML tag structuring, system prompt design, long-context window management (200k+ tokens), and hallucination reduction.
Introduction & Advanced Model Context Protocol (MCP)
Building bidirectional enterprise connectors using Anthropic's Model Context Protocol (MCP): connecting LLMs securely to local filesystems, databases, Git repositories, and internal REST APIs.
Building Enterprise Applications with the Claude API
Integrating Claude 3.5 Sonnet and Haiku via API: streaming completions, tool use (function calling), token budgeting, Amazon Bedrock, and Google Cloud Vertex AI deployment.
Apply AI at Work — OpenAI Academy
Practical organizational productivity with ChatGPT Enterprise: writing assistants, data analysis (Advanced Data Analysis / Code Interpreter), and custom GPTs for teams.
Agents & Workflows — OpenAI Academy
Designing enterprise agentic workflows with OpenAI: task delegation, multi-step agent reasoning, contextual boundary enforcement, tool integrations, and human-in-the-loop review checkpoints.
Build with AI — OpenAI Developer Platform
Developing production applications with OpenAI models: Structured Outputs, Assistants API with code interpreter and file search, Whisper speech-to-text, and embeddings.
Gemini for Google Cloud Learning Path
Accelerating enterprise cloud engineering with Google Gemini: Gemini Code Assist, infrastructure optimization, log analysis, and Google Cloud Vertex AI development.
Active Certifications & Accreditations
Frequently Asked Questions — GenAIOps, MLOps & Python Specialist
How does Steven's GenAIOps & MLOps specialization differ from standard prompt engineering courses?
Alongside 1-day executive & business productivity workshops (such as AB-730, AI-3025, and CLAUDE-101), Steven's core engineering tracks focus on GenAIOps, LLMOps, and MLOps in production: deploying open-weights and commercial models on Kubernetes using Kubeflow (LFS147) and Red Hat OpenShift AI (AI067), building custom Model Context Protocol (MCP) servers in Python/TypeScript, engineering RAG pipelines on AKS, and enforcing Purview/Defender security guardrails.
Does Steven teach Python from foundational automation up to AI/ML and MLOps?
Yes. Steven's custom Python curriculum (delivered on Cognitoz BetaLab) progresses from Python for Enterprise Systems & Automation (PY-ENT — data structures, REST APIs, subprocess automation, virtual environments) to Data Science (NumPy, Pandas, Scikit-Learn), PyTorch Deep Learning (LFD273), FastAPI/MCP server development, and Kubeflow/MLflow pipeline orchestration.
Can Steven advise on or architect a custom RAG or Agentic AI system?
Yes. Having engineered MISS2ASIA (AI-powered multi-tenant social media content & scheduling engine), Cognitoz AI (enterprise RAG platform on AKS), and ixvena (AI health OS), Steven provides architectural advisory, PoC development, and full-stack implementation for private enterprise AI systems.