Ai Training
Accelerate your team’s AI capabilities with hands-on, production-focused training. Learn through real case studies, practical coding exercises, and battle-tested patterns from systems deployed at scale.
Training is delivered on-site at your office or online to suit your preference. Choose from the modules below or request a tailored program combining multiple topics.
Stanford AI Professional Program contributor – Selected to present production AI systems including CV-Pilot, fine-tuning strategies, ReforgeAI (agentic code modernization), and Sentinel-AI (event monitoring) to 200+ attendees per session.
Training Programs
AI for Software Engineers
Practical AI development for engineers building production systems. Move beyond API calls to understand how AI works under the hood. Learn PyTorch fundamentals, build custom models, deploy at scale, and integrate AI into existing codebases.
What You’ll Learn:
- PyTorch essentials: tensors, autograd, custom neural networks
- Training loops, loss functions, optimization techniques
- Model serving with FastAPI and production deployment patterns
- Integration with existing software architectures (microservices, event-driven)
- GPU optimization and distributed inference basics
- MLOps foundations: experiment tracking, versioning, monitoring
Who It’s For: Backend, full-stack, and platform engineers looking to build AI features without relying solely on third-party APIs.
Format: Hands-on coding workshops with real codebase examples. Participants write and deploy working models.
LLM Engineering & Fine-Tuning
Master large language models from API integration to custom fine-tuning. Learn when to use prompt engineering, RAG, or fine-tuning—and how to implement each approach for production systems.
What You’ll Learn:
- LLM fundamentals: transformer architecture, attention mechanisms, tokenization
- Advanced prompt engineering and structured outputs
- RAG system design: chunking strategies, embedding models, vector databases (Qdrant/Milvus)
- Fine-tuning with LoRA/QLoRA on custom datasets
- Evaluation frameworks (Inspect AI, LangTrace, custom metrics)
- Production deployment: vLLM, Hugging Face TGI, cost optimization
- Distributed inference across multi-GPU setups
Who It’s For: Engineers and data scientists building LLM-powered applications, chatbots, or knowledge systems.
Real-World Project: Build and deploy a production RAG pipeline with custom fine-tuned embeddings.
Agentic AI & Multi-Agent Systems
Design and deploy autonomous AI agents that plan, decide, and act. Learn production patterns from deployed agentic systems including ReforgeAI (code modernization) and Sentinel-AI (event monitoring).
What You’ll Learn:
- Agentic AI architectures: ReAct, tool use, human-in-the-loop
- Multi-agent orchestration with CrewAI, LangGraph, AutoGen
- Event-driven agent communication (NATS, Kafka)
- Security and governance for autonomous systems
- Agent evaluation and debugging strategies
- Production deployment patterns and observability
Who It’s For: Engineers building autonomous systems, workflow automation, or AI-powered decision-making tools.
Case Study: Analyze production agentic systems serving real customers—architecture, challenges, and lessons learned.
Computer Vision & Multimodal AI
Build production computer vision systems from segmentation to custom object detection. Learn CNNs, Vision Transformers, and multimodal models with hands-on PyTorch implementation.
What You’ll Learn:
- CNN architectures: ResNet, EfficientNet, modern variants
- Vision Transformers (ViT) and when to use them
- Custom object detection with YOLO (training, fine-tuning, deployment)
- Semantic segmentation (Segformer, U-Net)
- OCR and document intelligence (Tesseract, DeepSeek-OCR, SmolDocling)
- Multimodal models: CLIP, vision-language understanding
- Production deployment: optimization, batch processing, real-time inference
Who It’s For: Engineers building computer vision features, image processing pipelines, or visual AI products.
Real-World Project: Train and deploy a custom object detection model for your specific use case.
Production AI & MLOps
Deploy and maintain AI systems at scale. Learn production patterns from systems serving millions of users—containerization, monitoring, GPU optimization, and incident response.
What You’ll Learn:
- Kubernetes for AI workloads with NVIDIA Container Toolkit
- Model serving: FastAPI, Docker, horizontal scaling
- Distributed inference optimization (vLLM, batching, caching)
- Observability: Grafana/Prometheus/Loki for AI services
- CI/CD for ML: experiment tracking, model versioning, automated testing
- Cost optimization: GPU utilization, batching strategies, quantization
- Production debugging and incident management
Who It’s For: ML engineers, platform engineers, and DevOps teams deploying AI to production.
Case Study: Architecture review of production AI platform serving 100M+ users.
AI Foundations
Build shared understanding of AI fundamentals across your team. Clear explanations of how generative AI works, where it helps, how to assess reliability, and what to expect next. Teams leave with common vocabulary to apply in daily work.
What You’ll Learn:
- How large language models and generative AI actually work
- Capabilities and limitations of modern AI systems
- When AI adds value (and when it doesn’t)
- Assessing AI tool reliability and accuracy
- Understanding hallucinations, bias, and failure modes
- AI landscape: what’s real vs. marketing hype
Who It’s For: Cross-functional teams, product managers, engineering leaders building shared AI knowledge.
Format: Interactive sessions with live demonstrations and Q&A.
Starting with AI Tools
Hands-on sessions using accessible AI tools and generative chatbots. Learn prompt engineering basics, effective use of off-the-shelf AI tools, and when not to use them.
What You’ll Learn:
- Practical prompt engineering techniques
- Effective use of ChatGPT, Claude, and other LLM tools
- Structured outputs and chain-of-thought reasoning
- Tool selection: which AI tool for which task
- Integration patterns for existing workflows
- Common pitfalls and how to avoid them
Who It’s For: Teams new to AI tools looking for practical, safe adoption strategies.
Platforms Covered: ChatGPT, Claude, GitHub Copilot, Cursor, and domain-specific tools on request.
Workflow Automation
Learn fundamentals of automating internal processes with accessible tools. Compare intelligent AI workflows with traditional automation and decide when to use each approach.
What You’ll Learn:
- Traditional automation vs. AI-powered workflows
- Event-driven architecture for workflow automation
- Integrating AI into existing business processes
- Error handling and human-in-the-loop patterns
- Monitoring and debugging automated workflows
- ROI assessment and optimization
Who It’s For: Operations teams, process owners, and engineers automating repetitive tasks.
Format: Workshop-style with real workflow examples from your organization.
Prompt Injection Security
Understand emerging AI security threats and how to defend against them. Prompt injection attacks can lead to data theft, manipulation, or unauthorized actions. Learn how these attacks work and steps to reduce risk.
What You’ll Learn:
- How prompt injection attacks work (direct and indirect)
- Real-world examples and attack vectors
- Business operations most at risk
- Defense strategies: input validation, output filtering, sandboxing
- Security best practices for LLM applications
- Incident response for AI security breaches
Who It’s For: Security teams, engineers building LLM applications, and technical leaders responsible for AI governance.
Format: Interactive demonstrations of attacks and defenses with hands-on mitigation implementation.
Responsible AI
Use AI safely and ethically in production. Generative AI can hallucinate, produce biased outputs, and expose sensitive data if mishandled. Learn secure practices, bias mitigation, and output validation.
What You’ll Learn:
- Understanding and mitigating AI hallucinations
- Recognizing and reducing bias in AI systems
- Data privacy and security best practices
- Testing outputs for accuracy and relevance
- Compliance considerations (GDPR, data governance)
- Building trust and transparency in AI systems
Who It’s For: Product teams, compliance officers, and engineering leaders deploying customer-facing AI.
Format: Case studies, ethical frameworks, and practical implementation guidelines.
AI for Business Leaders
Strategic AI adoption for decision-makers. Set adoption strategy, define controls and responsibilities, and support teams through AI-driven change. Improve quality and increase value while maintaining appropriate safeguards.
What You’ll Learn:
- AI strategy development and roadmap planning
- Assessing AI opportunities and risks in your organization
- Building vs. buying AI capabilities
- Governance frameworks and responsible AI policies
- Change management for AI adoption
- Measuring ROI and success metrics
- Team structure and hiring for AI initiatives
Who It’s For: CTOs, VPs of Engineering, product leaders, and executives driving AI transformation.
Format: Strategic workshops with industry benchmarks and decision frameworks.
Training Delivery
Flexible Formats:
- On-site workshops at your office (full-day or multi-day intensive)
- Virtual training with live coding and interactive sessions
- Hybrid programs combining self-paced learning with live instruction
- 1-on-1 coaching for individual contributors or technical leaders
Customization:
- Combine multiple modules into comprehensive programs
- Tailor content to your specific use cases and tech stack
- Include code reviews of your existing AI implementations
- Add domain-specific topics on request
What’s Included:
- Hands-on exercises with real code and datasets
- Production code examples and reference implementations
- Access to training materials and resources
- Post-training support and follow-up Q&A
Why This Training Works
✅ Production-focused – Learn patterns from systems deployed at scale, not toy examples
✅ Hands-on coding – Write, deploy, and debug real AI systems during training
✅ Battle-tested – Techniques from production systems serving millions of users
✅ Flexible delivery – On-site, online, or hybrid to match your team’s needs
✅ Stanford-caliber – Training quality trusted by top AI programs and enterprises
Ready to Upskill Your Team?
Build AI capabilities that drive real business value. Schedule a consultation to discuss your team’s needs and design a custom training program.
Schedule a Training Consultation
📧 Email: gianpaolo.santopaolo {at} gmail.com
💼 Experience: 24+ years software engineering, deep focus on generative AI since 2017
🎓 Teaching: Stanford AI Professional Program contributor (200+ attendees per session)
💻 Portfolio: CogniX | ReforgeAI | Sentinel-AI | DeltaE
All training programs emphasize production engineering patterns, proper testing, security best practices, and real-world deployment—preparing your team to build AI that scales.