People responsible for informed decisions
- AI product owners and Builders
- Evaluation and quality practitioners
- Model-risk, governance, and security reviewers
- Technical leaders responsible for AI release and monitoring
Batoi Learn Lab
AI Evaluation and Observability Lab
Design versioned AI evaluation evidence, calibrate evaluators, diagnose regressions, and define monitoring, release, rollback, and escalation decisions.
Individual developers can begin in a free, non-production Developer workspace. Team assignments, teaching, and governance features depend on plan and role.
Bring a real, bounded context
- A synthetic or approved sanitized AI use case
- Named users, task, failure consequences, and decision owner
- Stated quality, safety, cost, latency, and recovery expectations
Learning Outcomes
Develop reasoning that can withstand review
The Lab combines method, guided practice, evidence, assessment, reflection, and an applicable output.
Define the evaluation contract
Freeze the system, users, dimensions, thresholds, affected segments, owners, and stop conditions before comparing candidates.
Create versioned evidence
Build representative, boundary, adversarial, abstention, segment, and recovery cases with provenance and explicit ground-truth status.
Calibrate evaluators
Use deterministic, human, statistical, and model-assisted methods according to what each can validly establish.
Diagnose and decide
Preserve regressions, drift, disagreement, and quality-safety-cost-latency trade-offs in a reviewed release or recovery decision.
Guided Learning Path
Learn → Try → Assess → Reflect → Apply
Each stage strengthens the evidence and preserves the distinction between learning and operational authority.
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1
Learn
Review evaluation contracts, evidence classes, ground truth, calibration, regression, monitoring, and recovery.
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2
Try
Apply the method to a versioned synthetic AI candidate and case set.
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3
Assess
Test normal, boundary, adversarial, segment, trade-off, and rollback evidence.
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4
Reflect
Identify the adverse case or disagreement that most changes the decision.
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5
Apply
Prepare a versioned evaluation and monitoring decision pack for authorized Build review.
Evidence produced through the Lab
A versioned decision pack covering the evaluation contract, case-set provenance, ground truth, evaluator calibration, regression analysis, monitoring, incident triggers, release thresholds, rollback, limitations, and accountable decision.
Transparent criteria support improvement
- System and decision boundary
- Evaluation contract and thresholds
- Case-set coverage and provenance
- Ground-truth and evaluator discipline
- Regression, monitoring, recovery, and accountable decision
Feedback and reflection support retry and mastery; they do not replace destination review or approval.
Responsible Boundaries
Practice remains bounded and reviewable
A completed Lab demonstrates learning evidence. It does not grant production authority, certify compliance, accept risk, or bypass human decisions.
The Lab performs no model call, provider evaluation, deployment, or production monitoring.
Automated capstone feedback is a documentation signal and cannot establish correctness, proficiency, or release readiness.
Application requires deterministic ground-truth evidence or an approved consent-based human review, and Build revalidates current authority.
Frequently Asked Questions
About this Lab
No. The interactive score and capstone evaluator are coaching signals. Release requires exact evidence, valid ground truth or accountable review, and the owning product’s normal authority.
No. It uses versioned synthetic fixtures and reviewed planning evidence without contacting a provider or production system.
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