🔧AI in Production
How to get AI from notebook to production — reliably, safely, and cost-effectively.
What you'll learn
- Integrate OpenAI and Anthropic APIs with streaming and error handling
- Implement prompt management with versioning and A/B testing
- Build evaluation pipelines with automated scoring
- Optimize costs with caching, routing, and tier selection
- Set up monitoring, alerting, and quality dashboards
- Implement security guardrails and compliance controls
Who this is for
Software engineers, MLOps engineers, tech leads, and architects deploying AI systems to production.
Prerequisites
Syllabus
AI API Integration: OpenAI, Anthropic, and Local Models
SDKs, streaming, error handling, and retry logic — the fundamentals no production system survives without past week one.
Prompt Management: Prompts as Code
Versioning, A/B testing, and prompt registries — how to manage prompts that live in production and change faster than code.
Evaluating AI Outputs: Measuring Quality
Automated scoring, human-in-the-loop, and regression testing — the evaluation pipeline without which you are flying blind in production.
Cost Optimization: Token Budgets and Smart Routing
Token budgets, prompt caching, model routing, and tier selection — how to cut AI costs by 50-80% without losing quality.
Reliability: Retries, Fallbacks, and Graceful Degradation
Retries with backoff, provider fallbacks, guardrails, and circuit breakers — how to ensure your AI system survives the reality of production.
Monitoring and Observability for AI Systems
Logging, alerting, drift detection, and quality dashboards — how to know your AI system works correctly before users complain.
Security and Compliance for AI Systems
PII handling, data residency, SOC2 requirements, and red teaming — how to meet security and regulatory demands for AI in production.
We also offer live workshops and custom training for teams.
Learn more about team training