All lessons, quizzes, and hands-on labs are ready today. Instructor videos are being recorded based on Founder Edition member demand throughout Summer 2026. Founder Edition members get direct email support, first-mover vote on video production order, and lifetime pricing lock. Videos complete by end of October 2026, at which point “Beta” drops and the full Production launch begins.
AI Platform Engineering Bootcamp
🟡 Beta bootcamp: hands-on labs, transcripts, and quizzes are ready for all 8 weeks. Video lessons are coming soon. Content may be refined as we iterate based on student feedback. An 8-week intensive program designed to transition Platform Engineers into AI Platform Engineering and LLMOps roles. Build production-ready AI systems using LLMs, RAG, agents, and MLOps practices on Kubernetes infrastructure. The 8 weeks build toward a capstone Platform Assistant: a production-ready AI system that combines the LLM integration, RAG, agent, MLOps, model serving, and observability layers introduced across each week. Each course advances one layer of that capstone so you finish with a single coherent system, not a pile of disconnected exercises.
What you get today vs. what's coming
✅Ready today
- •All 8 course modules with complete lesson text and code examples
- •All quizzes (4-5 per module) with instant feedback
- •All ~30 hands-on labs, validated through 3 rounds of automated testing via the TeKanAid academy lab system
- •AI lab assistant available in every lab for hints and guidance
- •Direct email support from the instructor (24-hour weekday response for Founder Edition members)
- •Production-style capstone: AI Platform Assistant with RAG, agents, MCP, and Vault integration
⚠️Who should NOT buy this yet
If watching instructor videos is critical to your learning style, wait for the Production launch in October 2026. If you're ready to dig into hands-on labs with lesson text + quizzes + direct instructor access via email, Beta Early Access is built for you.
What You'll Master
Integrate LLMs into applications using OpenAI, Claude, and open-source model APIs
Deploy AI gateways with intelligent routing, caching, and cost tracking on Kubernetes
Design and implement RAG systems with vector databases and evaluation pipelines
Build production AI agents using LangChain and LangGraph with safety guardrails
Create MCP servers for standardized AI-infrastructure integration
Implement MLOps pipelines with experiment tracking and workflow orchestration
Deploy models to production using KServe with autoscaling and canary deployments
Monitor AI systems with custom metrics, evaluation frameworks, and drift detection
Implement guardrails and safety for production LLM applications
Apply enterprise security using HashiCorp Vault for secrets management
Who Is This Bootcamp For?
Platform Engineers pivoting to AI Platform Engineering
DevOps Engineers adding AI/ML infrastructure skills
Software Engineers building AI-powered applications
Site Reliability Engineers managing AI workloads
Cloud Engineers implementing MLOps practices
What You'll Build
Real portfolio artifacts you can put on your GitHub and show hiring managers.
AI Platform Assistant capstone
Production-ready AI system combining LLM routing with cost tracking, RAG pipeline with vector databases, autonomous agents with Kubernetes tool access and safety guardrails, and enterprise secrets management via Vault. The complete AI Platform Engineer portfolio project.
RAG pipeline with evaluation
End-to-end Retrieval-Augmented Generation service with document chunking, embeddings, vector database storage, streaming API responses, and an automated evaluation pipeline to measure retrieval quality and answer accuracy.
AI observability stack
Comprehensive monitoring for AI systems with custom Prometheus metrics, Grafana dashboards for LLM latency and cost tracking, automated evaluation pipelines, model drift detection, and production guardrails for content safety.
Bootcamp Curriculum
Week 1: AI Foundations for Infrastructure Engineers
Bridge the gap between traditional infrastructure and AI systems. Run your first local LLMs and understand their resource requirements.
Goals:
- •Understand AI workloads from an infrastructure perspective
- •Master essential ML vocabulary and concepts
- •Deploy and interact with local LLMs using Ollama
- •Set up Python development environment for AI workloads
Week 2: LLM Integration and API Patterns
Build production-ready API layer with multi-provider routing, failover, caching, and cost tracking.
Goals:
- •Master LLM API integration patterns with multiple providers
- •Deploy AI gateway on Kubernetes with intelligent routing
- •Implement prompt engineering for production systems
- •Build cost tracking and optimization dashboards
- •Apply managed AI platform and serverless endpoint patterns
Week 3: RAG Architectures and Vector Databases
Connect LLMs to organizational knowledge bases with semantic search and optimized retrieval pipelines.
Goals:
- •Deploy and manage vector databases on Kubernetes
- •Implement document processing and chunking strategies
- •Build complete RAG API services with streaming
- •Evaluate and test RAG system quality
Week 4: AI Agents and Agentic Workflows
Build autonomous AI agents that can reason, plan, and execute complex tasks with proper safety guardrails.
Goals:
- •Master agent fundamentals and the ReAct pattern
- •Build Platform Engineering agents with Kubernetes tools
- •Implement LangGraph workflows with human-in-the-loop
- •Create MCP servers for standardized tool integration
- •Design multi-agent systems for complex tasks
Week 5: ML Infrastructure and Experiment Tracking
Implement experiment tracking, model versioning, and ML pipeline orchestration using GitOps principles.
Goals:
- •Deploy MLflow on Kubernetes with S3 artifact storage
- •Track LLM experiments and prompt strategies
- •Build ML pipelines with Argo Workflows
Week 6: Model Serving and Kubernetes for ML
Deploy models to production on Kubernetes with KServe, autoscaling, and canary deployments.
Goals:
- •Understand GPU scheduling concepts for ML workloads
- •Configure resource management for inference
- •Deploy models with KServe and autoscaling
- •Implement canary deployments for safe rollouts
Week 7: AI Observability and LLMOps
Implement comprehensive monitoring, evaluation, guardrails, and drift detection for AI systems.
Goals:
- •Deploy AI observability stack with Prometheus and Grafana
- •Build LLM evaluation pipelines with automated testing
- •Implement production guardrails and safety measures
- •Detect and respond to model drift and degradation
Week 8: Enterprise AI and Capstone Project
Apply all learned skills to build a production-ready AI-powered Platform Assistant with enterprise security.
Goals:
- •Implement enterprise AI security with Vault integration
- •Understand AI governance and compliance requirements
- •Complete comprehensive capstone project
- •Present production-ready Platform Assistant
Prerequisites
Completion of Platform Engineering Bootcamp or equivalent experience
Strong Kubernetes fundamentals (deployments, services, Helm)
Experience with Terraform and infrastructure as code
CI/CD pipeline experience (GitHub Actions preferred)
Python programming fundamentals (functions, classes, packages)
AWS cloud experience
Basic SQL knowledge (SELECT, JOIN, WHERE, GROUP BY)
Experience with relational databases (PostgreSQL or MySQL)
Frequently Asked Questions
What does Beta Early Access mean for this AI bootcamp?
All 8 modules of lesson content, quizzes, and ~30 hands-on labs are ready and usable today. Instructor videos are being recorded through Summer 2026 based on Founder Edition member votes. You can work through the entire curriculum right now — the written lessons include detailed code examples, architecture diagrams, and step-by-step explanations. Videos are a value enhancement, not a prerequisite for learning.
What prerequisites do I actually need?
Strong Kubernetes fundamentals (deployments, services, Helm), Terraform and infrastructure-as-code experience, Python basics (functions, classes, packages), AWS cloud experience, CI/CD pipeline experience (GitHub Actions preferred), and basic SQL knowledge. If you have completed the Platform Engineering Bootcamp or have equivalent production experience, you are ready. The Path Assessment quiz on our Start Here page can help you confirm.
Do I need GPUs or expensive cloud resources to run the labs?
No. Most labs use open-source models via Ollama that run on CPU. Labs that use hosted LLM APIs (OpenAI, Claude) work through the TeKanAid AI Gateway which provides API access at no additional cost to you — every lab environment has pre-configured API keys. No personal API keys or GPU instances are required.
What do I actually build during the 8 weeks?
A production-ready AI Platform Assistant. It combines an LLM API routing layer with cost tracking, a RAG system with vector databases for searching your internal documentation, autonomous AI agents built with LangChain and LangGraph that can query Kubernetes clusters with safety guardrails, MCP servers for standardized tool integration, MLOps pipelines with MLflow, model serving with KServe, and enterprise secrets management via HashiCorp Vault. By the end, you have a complete portfolio project.
How does this compare to a typical MLOps or data science course?
Typical MLOps courses teach the data science side — training models, feature engineering, experiment tracking. This bootcamp teaches the platform engineering side — how to run AI systems in production on Kubernetes with proper networking, secrets management, observability, cost controls, and safety guardrails. It is designed for infrastructure engineers who need to support AI workloads, not for data scientists. The two are complementary.
Is 200 hours really 25 hours per week for 8 weeks?
200 total hours at 25 hours per week is the intensive full-time pace. Most working professionals take 4-6 months at 5-10 hours per week on evenings and weekends. The bootcamp is entirely self-paced — there is no cohort schedule. Your Founder Edition pricing stays locked as long as your subscription is active, so take as long as you need.
What if I hit a broken lab or unclear lesson during Beta?
Email Sam directly. Founder Edition members get a 24-hour weekday response time on Beta support issues. AI infrastructure moves fast and we want to keep the content current — every report helps improve the bootcamp for everyone.
Can I start this bootcamp without doing the Platform Engineering Bootcamp first?
Yes, if you already have the prerequisites (Kubernetes, Terraform, Python, AWS, CI/CD). The AI bootcamp does not directly depend on completing the PE bootcamp — it depends on having equivalent skills. If you are unsure, take the Path Assessment quiz on our Start Here page.
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