Description

A focused course, tailored for you

The Research Scientist Launch-Review Evidence Pack

Author the model evaluation pack that survives responsible-AI launch review on first pass, with reproducible methodology, dataset provenance, fairness and robustness results, and a clean trade-off memo.

The model works. The evaluation pack does not yet exist. The launch review is on the calendar.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

A research scientist who has shipped models inside a large platform knows the pattern. The experiment converges, the offline metrics are strong, the team is ready to push for launch. Then the responsible-AI reviewer asks for the evaluation pack: dataset provenance, slice metrics across protected groups, robustness under distribution shift, red-team findings, the trade-off memo. The pack does not exist as a single artefact. It exists as fragments across notebooks, Slack threads, and the dataset team's wiki. Pulling it together for the review costs a week of work that should have happened alongside the experiment. Worse, the reviewer sometimes finds that a slice was not measured at all, which sends the whole launch back to the queue. This course teaches the discipline of authoring the pack as you run the experiment, so the launch review is a 30-minute confirmation rather than a two-week scramble.

What you walk away with

Author a launch-review-ready evaluation pack alongside the experiment, not after.
Produce a dataset card that names every source, consent basis, and known limitation the reviewer asks about.
Run a slice analysis that surfaces the cells the reviewer would have flagged, before the reviewer sees them.
Run a robustness probe set that maps to the deployment distribution rather than the training distribution.
Write a trade-off memo that names the metric you traded for the metric you protected, with the math to back it.
Cut launch-review cycle time from weeks to a single 30-minute confirmation.

The 12 modules

Module 1. The launch-review artefact pack as a deliverable

Reframes the evaluation pack as the actual deliverable the research scientist owns, not a side artefact produced after the model is done. Names the eight components a typical platform-launch reviewer expects, the order in which they should be authored, and the moment in the experiment cycle each one is cheapest to write. Includes a worked artefact-pack table of contents and a self-audit checklist a research scientist runs against their own work-in-progress before the review request goes out.

Module 2. Methodology memo with seeds, config hashes, and runbook

Teaches the methodology memo that lets a reviewer reproduce the result without asking you a single question. Covers the seed log, the config hash, the data snapshot identifier, the eval script entry point, and the runbook a fresh engineer follows to get the same numbers. Includes a downloadable template and a worked example for a fine-tuning experiment on a foundation model, with the specific fields a reviewer typically reads first.

Module 3. Dataset card with sources, consent basis, and known limitations

The dataset card is the artefact that sinks more launches than the model itself. Module walks through naming every data source, recording the consent or licensing basis for each, documenting known limitations and demographic gaps, and stating the data refresh policy. Includes the format reviewers expect, the questions a privacy reviewer asks first, and a worked dataset card for a multi-source training corpus including third-party licensed data.

Module 4. Slice metrics across protected and product-relevant groups

A reviewer reads the slice table before the headline metric. Module covers selecting the slice dimensions that matter for the deployment, defining the minimum cell size for a stable metric, picking the right metric per slice (calibration vs. ranking vs. classification), and writing the explanatory notes that name the cells that look concerning and why. Includes a slice table template and a worked slice analysis on a ranking model.

Module 5. Robustness probes mapped to the deployment distribution

Training-distribution robustness numbers do not survive review. Module teaches building a probe set that maps to the actual deployment distribution: adversarial perturbations for the input modality, natural shift simulators, prompt-injection probes for LLM-style models, and the way to report each so the reviewer can see what the model handles and what it does not. Includes a probe library and a worked robustness report.

Module 6. Red-team log and known-failure catalogue

Reviewers expect a red-team log that names the prompts or inputs that broke the model and the mitigation or accepted-risk decision for each. Module covers running a structured red-team session, logging findings in a format that maps cleanly to the mitigation tracker, and writing the known-failure catalogue that documents accepted risks for the launch reviewer. Includes the red-team log template and worked entries for a generative model.

Module 7. Privacy review artefacts: data flow, retention, and DSR posture

Privacy review is separate from responsible-AI review and asks different questions. Module walks through the data flow diagram a privacy reviewer expects, the retention policy mapping for training and eval data, the data subject request posture for any user-derived training data, and the specific artefacts that satisfy each privacy reviewer question. Includes templates and a worked example for a model trained on user-derived signals.

Module 8. Trade-off memo: what you optimised, what you traded

The memo that makes a launch reviewer trust the result. Names the metric you optimised, the metric you traded against it, the math behind the trade, and the explicit recommendation the reviewer is being asked to sign. Module includes the trade-off memo template, the rhetorical moves that work and the ones that get pushed back, and a worked memo for a precision/recall trade on a safety classifier.

Module 9. Reproducibility audit: the reviewer's repro script

A subset of launch reviewers run a repro script before the meeting. Module teaches authoring a repro script the reviewer can execute from a clean environment, with the data fetch step, the model load step, and the eval invocation. Covers what gets pinned, what gets parameterised, and how to write the README that lets a reviewer get a result in under 30 minutes. Includes a repro script skeleton and a worked example.

Module 10. Evaluation pack assembly: from fragments to one document

The eight components above need to read as one document, not eight fragments. Module covers the assembly discipline: the executive summary the reviewer reads first, the cross-reference table that maps each reviewer concern to the artefact that addresses it, the limitations section that names what you did not test, and the version control discipline that keeps the pack in lockstep with the model. Includes the assembly template and a worked full pack.

Module 11. The launch-review meeting itself: 30 minutes, not two hours

If the pack is right, the meeting is short. Module covers running the launch-review call: the three slides that confirm the headline trade-off, the question types reviewers ask first, the answer formats that close concerns versus reopen them, and the decision-memo capture that goes back to leadership. Includes the meeting agenda template and a worked transcript of a successful launch review.

Module 12. The post-launch evidence loop: monitoring that closes the audit trail

Launch is not the end of the artefact pack. The reviewer expects a monitoring plan that closes the loop: which slice metrics get logged in production, which drift signals trigger a recheck, the criteria that send the model back to review, and the cadence of the post-launch artefact refresh. Includes the monitoring plan template and a worked post-launch artefact-pack v1.1 entry for a recommendation model.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 names what the pack actually is. Modules 2 through 4 are the methodology and dataset core a reviewer reads first.

Modules 5 and 6 are the failure-mode artefacts: robustness probes and the red-team log.

Modules 7 through 9 are the cross-functional artefacts: privacy posture, the trade-off memo, and the reproducibility audit.

Modules 10 through 12 close the loop: pack assembly, the review meeting itself, and the post-launch monitoring artefact that keeps the pack valid.

What you get with this course

12 written modules, each with downloadable artefact templates.
Worked end-to-end example evaluation pack for a representative model class.
Repro script skeleton, slice table template, dataset card template, trade-off memo template, red-team log template, privacy artefact set, monitoring plan template.
Hand-built implementation playbook tuned to the model class and review process the buyer names at intake.
30-day money-back if the pack does not cut review cycle time.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Module 1 through 4 can be worked in the first week alongside an active experiment.

Modules 5 through 9 are paced across weeks two and three; they include the artefacts most launches fail on.

Modules 10 through 12 align with the actual launch-review meeting; the implementation playbook tracks the specific review process the buyer names at intake.

Before and after

Before

Evaluation results sit in a notebook. The pack is assembled the week before the launch review by stitching fragments from Slack, the dataset team's wiki, and three different colabs. The reviewer asks for a missing slice analysis. The launch slot moves.

After

The pack is authored alongside the experiment. The reviewer opens one document that answers every standard question and names every limitation up front. The launch review is a 30-minute confirmation. The model ships on the original slot.

What happens if you do not address this

Without the discipline, every launch review costs one to two weeks of pack-assembly work that should have happened alongside the experiment. Over a year, that is two to four launches missed or delayed per scientist. The cost is not the work itself; it is the slot the model sits in while waiting for a review the pack failed to satisfy on the first pass.

Who it is for

A research scientist inside a large platform whose models go through an internal launch review with responsible-AI and privacy sign-off. Comfortable with the modelling work itself. Less comfortable with the documentation discipline that the launch reviewer requires. Wants the pack to land clean on the first submission.

Who this is NOT for. Researchers in pure academic settings where the output is a paper, not a launched model. Engineers who own model deployment but not the evaluation. Anyone whose review process does not require a responsible-AI sign-off artefact pack.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. About 12 to 16 hours of reading and template work across the 12 modules. The implementation playbook adds 2 to 4 hours of buyer-specific setup, paced against the buyer's next launch-review cycle.

Why $199 is the right number

Internal launch-review guides describe what reviewers expect but do not teach the authoring discipline. Public responsible-AI papers describe the principles but not the artefact format. Generic ML evaluation courses cover offline metrics but stop short of the pack the reviewer opens. This course teaches the authoring discipline as the deliverable.

FAQ

Is this useful if my organisation's launch-review process looks different?

The artefact set is the same set responsible-AI and privacy reviewers ask for in every large-platform process. The implementation playbook is tuned to the specific review process the buyer names at intake.

Does it cover LLM-specific evaluation?

Modules 5 and 6 cover prompt-injection probes, red-team logs for generative models, and the known-failure catalogue patterns specific to LLMs. The worked example uses a generative model where it adds clarity.

Does it cover fairness metrics?

Module 4 covers slice-metric selection, including the calibration and ranking metrics the reviewer expects per protected group and per product-relevant slice.

What if my model already shipped?

Module 12 covers the post-launch artefact loop. The pack discipline applies to any model still in production review cycles.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.