Advanced Ollama Model Debugging & Evaluation Training Course
Advanced Ollama Model Debugging and Evaluation is a comprehensive course designed to diagnose, test, and measure the behaviour of models during local or private Ollama deployments.
This instructor-led, live training (available online or on-site) targets advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to guarantee the reliability, fidelity, and operational readiness of Ollama-based models in production environments.
By the conclusion of this training, participants will be able to:
- Conduct systematic debugging of Ollama-hosted models and reliably reproduce failure modes.
- Design and execute robust evaluation pipelines incorporating quantitative and qualitative metrics.
- Implement observability measures (logs, traces, metrics) to monitor model health and detect drift.
- Automate testing, validation, and regression checks integrated into CI/CD pipelines.
Course Format
- Interactive lectures and discussions.
- Hands-on labs and debugging exercises utilising Ollama deployments.
- Case studies, group troubleshooting sessions, and automation workshops.
Course Customisation Options
- To request customised training for this course, please contact us to make arrangements.
Course Outline
Introduction and Diagnostic Foundations
- Overview of failure modes in LLM systems and common Ollama-specific issues
- Establishing reproducible experiments and controlled environments
- Debugging toolset: local logs, request/response captures, and sandboxing
Reproducing and Isolating Failures
- Techniques for creating minimal failing examples and seeds
- Stateful versus stateless interactions: isolating context-related bugs
- Determinism, randomness, and controlling non-deterministic behaviour
Behavioural Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative evaluations: 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, scenario and end-to-end tests
- Creating 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, 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 addressing dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigations: 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
- Substantial experience building and deploying LLM applications
- Familiarity with Ollama workflows and model hosting
- Proficiency with Python, Docker, and basic observability tooling
Audience
- AI engineers
- ML Ops professionals
- QA teams responsible for production LLM systems
Need help picking the right course?
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Advanced Ollama Model Debugging & Evaluation Training Course - Enquiry
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