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Duration 35 hours
Course Outline
Foundations of Introduction and Diagnostics
- Overview of failure patterns in LLM systems and specific Ollama-related challenges.
- Setting up reproducible experiments and controlled testing environments.
- Debugging toolkit: analyzing local logs, capturing request/response data, and sandboxing techniques.
Reproducing and Isolating Defects
- Methods for creating minimal reproducible examples and seed data.
- Managing stateful versus stateless interactions to isolate context-dependent bugs.
- Controlling determinism, randomness, and non-deterministic behaviors.
Behavioral Evaluation and Metrics
- Quantitative measures: accuracy, ROUGE/BLEU variants, calibration, and perplexity indicators.
- Qualitative assessments: designing human-in-the-loop scoring and evaluation rubrics.
- Task-specific fidelity checks and defining acceptance criteria.
Automated Testing and Regression Analysis
- Writing unit tests for prompts and components, along with scenario and end-to-end tests.
- Building regression suites and establishing baselines using golden examples.
- Integrating Ollama model updates into CI/CD with automated validation gates.
Observability and Monitoring
- Implementing structured logging, distributed tracing, and correlation IDs.
- Key operational metrics: latency, token consumption, error rates, and quality signals.
- Configuring alerts, dashboards, and SLIs/SLOs for model-driven services.
Advanced Root Cause Analysis
- Tracing issues through prompt graphs, tool calls, and multi-turn interactions.
- Conducting comparative A/B diagnostics and ablation studies.
- Investigating data provenance, debugging datasets, and resolving dataset-induced errors.
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Implementing rollback, canary, and phased rollout patterns for model updates.
- Conducting post-mortems, capturing lessons learned, and fostering continuous improvement.
Summary and Future Directions
Requirements
- Extensive experience in developing and deploying LLM-based applications.
- Proficiency with Ollama workflows and model hosting practices.
- Working knowledge of Python, Docker, and fundamental observability tools.
Target Audience
- AI Engineers.
- MLOps Professionals.
- QA Teams overseeing production LLM systems.
