ML Engineering & MLOps Pack

What's inside

1. Prompt
   Machine Learning Problem Framing and Baseline Plan

   Frames a business problem as an ML task — target, labels, features, evaluation metric tied to business value, leakage risks — and plans a simple baseline before anyone trains a deep model.

2. Prompt
   Model Evaluation and Error Analysis Report

   Evaluates a trained model beyond one score — calibration, slices, error categories, threshold choice, comparison to baseline, robustness — and writes a decision-ready report.

3. Prompt
   ML Model Deployment and Serving Design

   Designs how a model goes to production — batch or real-time serving, packaging, feature consistency, latency and scaling, versioning, rollout, fallbacks and monitoring hooks.

4. Prompt
   ML Monitoring, Drift Detection and Retraining Plan

   Sets up monitoring for production models — data quality, input drift, prediction drift, performance with delayed labels, alerts and a retraining policy — so models don't silently decay.

5. Prompt
   ML Training Pipeline Reproducibility Review

   Reviews a training pipeline for reproducibility — data versioning, seeds, environment pinning, experiment tracking, config management and data leakage — so results can be trusted and repeated.

6. Prompt
   LLM Feature or Classic ML Model? A Decision Memo

   Decides whether a product feature should use a large language model, a classic ML model, rules, or a mix — weighing accuracy, cost per request, latency, data needs, control and risk.

7. Skill
   ML Engineer and MLOps Lead

   A standing ML engineer: problem framing, evaluation, deployment, monitoring, reproducibility and LLM-versus-ML decisions — honest metrics, no leakage, every model versioned and monitored.

From the wider PromptCorrectly library, picked for the same work. They come in the same .zip and the start file tells your AI when to use each one.

1. Skill
   Exploratory Data Analysis Strategist

   Produces a structured EDA plan and narrative findings for any new dataset before modeling.

   Library · Free account
2. Skill
   AI Builder Eval Harness

   Designs a small, honest eval harness for a prompt, skill, or agent so the builder stops shipping on vibes. Use before calling anything 'production'.

   Library · In a pack
3. Prompt
   Reproducible Data Management Plan

   Drafts a funder-ready data management plan covering collection, storage, sharing, ethics, and long-term preservation.

   Library · In a pack
4. Prompt
   Critical Source Evaluation and Bias Audit

   Appraises a source's credibility, methodology, and bias using structured criteria to decide how to use it responsibly.

   Library · In a pack
5. Prompt
   Messaging A/B Test Hypothesis Generator

   Generates rigorous A/B test hypotheses for marketing messaging with variants, success metrics, and sample logic.

   Library · In a pack
6. Prompt
   Cross-Functional Feature PRD Drafter

   Drafts a lean product requirements doc tying user problem, scope, UX flow, success metrics, and open questions together.

   Library · Free
7. Prompt
   RAG-Grounded Dashboard Insight Summarizer

   Summarizes dashboard data into a stakeholder narrative grounded strictly in supplied figures, citing each claim.

   Library · In a pack

A look inside

ROLE: You are a senior ML engineer who stops projects from failing at the framing stage. You insist on a clear target, honest evaluation and a simple baseline, because most ML failures are bad problem definitions, not bad models.

CONTEXT: The business problem and the decision …

Questions