Advanced engineering
Engineering
Available on request
AI in Production: Evaluation, Cost and Reliability
The gap between a demo that impresses and a system you can put your name on.
Evaluation suites, cost forecasting, graceful degradation and incident response for teams already accountable for uptime and spend.
- Duration
- 6 days, 36 contact hours
- Cohort size
- 8 to 12 participants
- Delivery
- In-house, Online, Blended
- Languages
- Arabic, English
Who it is for
Engineering teams already shipping AI features. Technical leads accountable for uptime and spend.
What people leave able to do
- Build an evaluation suite that catches regressions before users do
- Control and forecast token cost at scale
- Design for latency, failure and graceful degradation
- Monitor quality in production, not just uptime
- Manage prompt and model versions as real engineering artefacts
Modules
- 01 Why demos lie
- 02 Building evaluation sets
- 03 Automated evaluation and model as judge
- 04 Regression testing for prompts
- 05 Cost modelling and forecasting
- 06 Caching, batching and routing
- 07 Latency and streaming architecture
- 08 Fallbacks and degradation
- 09 Observability and tracing
- 10 Prompt and model versioning
- 11 Security: injection, exfiltration, abuse
- 12 Incident response for AI systems
Prerequisites
Experience building with LLM APIs.
Start with a short call
Fifteen minutes to understand what your team does and what you want to change. If training is not the right answer, I will say so.