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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Comprehensive review of systemic failure modes in Large Language Models and specific operational challenges associated with Ollama infrastructure
- Establishing rigorous experimental protocols to ensure reproducibility within controlled testing environments
- Deployment of specialized debugging resources, including localized logging, request/response inspection, and sandboxed execution contexts
Reproduction and Isolation of Faults
- Methodologies for constructing minimal reproducible cases and identifying root cause seeds
- Distinction between stateful and stateless interactions for the precise isolation of context-dependent defects
- Management of determinism and stochastic elements to control non-deterministic system behavior
Behavioral Assessment and Performance Metrics
- Application of quantitative indicators, including accuracy benchmarks, ROUGE/BLEU variants, calibration scores, and perplexity proxies
- Implementation of qualitative assessments through human-in-the-loop scoring and structured rubric development
- Validation of task-specific fidelity and establishment of defined acceptance criteria
Automated Verification and Regression Testing
- Development of unit tests for prompts and system components, alongside comprehensive scenario and end-to-end validations
- Construction of regression test suites and baselines using golden examples
- Integration of Ollama model updates into CI/CD pipelines with automated validation gates for government-compliant release management
Observability and Continuous Monitoring
- Implementation of structured logging, distributed tracing, and correlation ID tracking for auditability
- Monitoring of critical operational indicators, including latency, token consumption, error frequency, and quality assurance signals
- Configuration of alerting mechanisms, visual dashboards, and Service Level Indicators/Service Level Objectives (SLIs/SLOs) for model-driven services
Advanced Root Cause Analysis
- Deep-dive tracing of prompt graphs, tool invocations, and multi-turn conversational flows
- Conducting comparative A/B diagnostics and ablation studies to identify specific component contributions to failure
- Analysis of data provenance, dataset integrity, and remediation of dataset-induced system anomalies
Safety, Robustness, and Corrective Actions
- Application of mitigation strategies such as content filtering, factual grounding, retrieval augmentation, and prompt scaffolding
- Execution of rollback procedures, canary releases, and phased deployment patterns for model version updates
- Conducting post-incident reviews, capturing lessons learned, and establishing continuous improvement cycles for accountability
Summary and Forward-Looking Recommendations
Requirements
- Extensive experience in the development and deployment of Large Language Model applications
- Proficiency in Ollama workflows and model hosting architectures
- Competence in Python, Docker, and foundational observability tooling
Target Audience
- AI Engineers
- MLOps Professionals
- QA Teams responsible for the oversight of production LLM systems
