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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Analysis of failure modes in LLM systems and frequent Ollama-specific challenges
- Creating reproducible experiments and controlled environments
- Debugging toolkit: local logs, request/response captures, and sandboxing techniques
Reproducing and Isolating Failures
- Methods for generating minimal failing examples and seeds
- Distinguishing stateful from stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and non-deterministic behaviour
Behavioural Evaluation and Metrics
- Quantitative indicators: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric design
- Task-specific fidelity checks and acceptance criteria
Automated Testing and Regression
- Unit tests for prompts and components, as well as scenario and end-to-end tests
- Building regression suites and golden example baselines
- CI/CD integration for Ollama model updates and automated validation gates
Observability and Monitoring
- Structured logging, distributed traces, and correlation IDs
- Key operational metrics: latency, token usage, error rates, and quality signals
- Alerting systems, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn flows
- Comparative A/B diagnosis and ablation studies
- Data provenance, dataset debugging, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and phased rollout patterns for model updates
- Post-mortems, lessons learned, and continuous improvement loops
Summary and Next Steps
Requirements
- Extensive experience in building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting
- Working knowledge of Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- MLOps Professionals
- QA Teams responsible for production LLM systems
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 8000 € + VAT*
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