AI Platform Engineering Bootcamp
A 16-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.
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
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
- •Integrate AWS Bedrock for managed AI services
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)
Technologies Covered
Choose your plan
Simple, Transparent Pricing
One price, everything included
Monthly Plan
Access all content
Quarterly Plan
Save 16% with quarterly billing
Everything Included in Your Subscription
Content & Learning
- Access to all courses and bootcamps
- Video lessons with closed captions
- Interactive quizzes and assessments
- Course completion certificates
Hands-On Labs
- Browser-based cloud labs
- Pre-configured VMs ready to use
- Playgrounds for experiments
- Multi-VM realistic scenarios
AWS Integration
- Managed AWS Account included
- Pre-configured environments
- Real-world cloud scenarios
Support & Community
- Priority support
- Active community forum
No Setup Required
- Everything runs in your browser
- No software installation needed
- Automatic environment provisioning
- Works on any device
Ready to Transform Your Career?
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