Corporate AI & Data Engineering Training
Advanced technical bootcamps for engineering teams.
Focus on production architecture, system constraints, and operational reality.
Target Audience
This is not an introductory course. It is structured upskilling for technical professionals building production systems.
Core Training Tracks
Organized by architecture layer. Customizable to team needs.
AI Infrastructure Engineering
- › LLM system architecture & RAG pipelines
- › Model serving & inference scaling
- › GPU cost optimization strategies
- › Observability for probabilistic systems
Data Platform Engineering
- › Modern data stack architecture
- › Batch vs. streaming tradeoffs
- › Data reliability engineering (DRE)
- › Metadata management & governance
Distributed Systems & Cloud
- › Kubernetes for data workloads
- › Scaling microservices & failure analysis
- › Infrastructure as Code (IaC) discipline
- › Multi-cloud architectural considerations
MLOps & Production AI
- › CI/CD pipelines for ML systems
- › Model versioning & feature stores
- › Monitoring drift & performance
- › Deployment strategies (Canary, Shadow)
Methodology
- 01 Architecture-first teaching
- 02 Real production production examples
- 03 Tradeoff-driven learning
- 04 Systems breakdown & design exercises
Delivery Formats
- Corporate on-site workshops
- Remote live training series
- Multi-day architecture bootcamps
- Executive technical briefings
Sample Curriculum
AI Infrastructure Bootcamp – 5 Day Intensive
System Architecture & Lifecycle
Modern AI platform architecture, LLM lifecycle overview, infrastructure constraints and capacity planning.
Inference & Compute
Model serving strategies, inference scaling, GPU cost implications, batching and caching patterns.
Retrieval Systems (RAG)
RAG system design, vector storage tradeoffs, retrieval optimization, hybrid search architectures.
Observability & Reliability
Monitoring probabilistic systems, failure mode analysis, evaluation frameworks, circuit breakers.
Governance & Production
Cost governance, platform abstraction, security/compliance, enterprise rollout strategy.
Corporate Outcomes
Reduced Misdesign
Avoid costly architectural mistakes before implementation begins.
Lower Inference Costs
Optimize token usage and GPU resource allocation.
Faster Productionization
Move from proof-of-concept to stable production systems reliability.