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IntermediateLLMOps & Evaluation Free Verifiable Certificate
LLM Evaluation & Production LLMOps
Measure, monitor and safely ship language-model features with evaluation sets, calibrated graders, tracing and release gates.
49 min of material
2 Modules (4 Lessons)
Free · no account needed to read
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The assessment unlocks once you have worked through the lessons.
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What You Will Master
- Build a versioned evaluation set with stable case IDs, slices, hard checks and written rubrics
- Calibrate a model-based grader with Cohen's kappa and false-pass rate before trusting it
- Instrument model calls as traces and read latency as percentiles rather than averages
- Attribute token cost to requests and find the retries and prompts that drive spend
- Write release gates with slice thresholds and honest confidence intervals
- Plan shadow and canary rollouts with pinned versions and a rehearsed rollback
Skills Acquired
LLM EvaluationModel-Based GradingLLM ObservabilityLatency & Cost AnalysisRelease GatingCanary Rollouts
Prerequisites
- Basic Python: functions, lists, dictionaries, and running a script from a terminal
- Experience calling a language model from code; the free prompting course is a useful starting point
- Python 3.10 or later for the lesson scripts; no API key or paid service is required
Curriculum Syllabus
2 modules · 4 lessons · 49 min · one assessment
Module 1
Module 1: Measuring Quality
Turn subjective review into versioned evaluation sets, automated checks and calibrated graders.
Module 2
Module 2: Operating in Production
Trace latency and cost, then ship changes through written gates, staged rollouts and rollback.
Course author
Md. Rajib Hawlader
Principal AI Research Scientist, NeuroLearn AI Labs
Writes and maintains the NeuroLearn AI curriculum.
Who am IReady to Begin LLM Evaluation & Production LLMOps?
Self-paced curriculum with free instant certificate issuance upon passing the evaluation.
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