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Production ML Checklist
Checklist for a production ML portfolio project with reproducible training, tracked runs, registry handoff, deployment, monitoring, and rollback criteria.
Related Wiki Pages
A production ML project proves that a model can leave the notebook without losing reproducibility, ownership, or observability. It should show the problem framing and baseline from Machine Learning Portfolio Projects. For ML-heavy projects, a compact ML System Design Documents writeup can capture the decision, non-goals, and baseline. It can also name the evaluation plan, serving mode, monitoring signals, and owner before the implementation checklist starts.
Use notebook-to-production workflow for the broader handoff sequence. This checklist adds the MLOps evidence that matters for ML platforms and machine learning engineering. That evidence includes tracked runs and artifact promotion. It also includes deployment, monitoring, and a rollback or retraining rule.
Data scientists may use a production project to cross into that role. Pair this checklist with data scientist to machine learning engineer. Software engineers often start from delivery, testing, and API habits. Use ML for Software Engineers to identify the ML and data skills to add before the checklist becomes realistic. For a broader path, use the ML engineer roadmap to sequence the project from modeling through deployment, monitoring, and production ownership.
This checklist fits the broader Portfolio Projects hub when the project is meant to prove production readiness rather than only model quality. The lifecycle runs from training and evaluation to experiment tracking and the model registry. It separates batch and online deployment. It also ties lineage metadata to prediction APIs and logs [1]. Use the Data Science Project Guide before this checklist when the project still needs scope, acceptance criteria, stakeholder ownership, or a stop/ship decision.
When the same review standard is applied to LLM-backed products, use AI Engineering Portfolios for the software evidence. It also covers evaluation, retrieval, and operations evidence.
Lifecycle Proof
The project should turn a decision problem into a maintained model artifact.
A credible implementation records:
- the code version and data reference
- parameters and dependencies
- the evaluation result and saved artifact
- the deployment target, monitoring signals, and owner action for rollback or retraining
That’s the full lifecycle scaled down to a reviewable portfolio repository [1]. For an industrial project, manufacturing predictive maintenance and yield analytics shows the same checklist in domain form. It connects telemetry with a baseline qualification schedule. It also names the forecasted risk window and the engineer-facing action [2].
The lightweight standard puts Git and CI/CD in the essential stack. The same stack includes artifact storage and registries. It also needs documentation, reproducibility, code quality, and testing [3].
Notebook logic should move into packages and CI/CD [3]. A portfolio project can stay small, but it shouldn’t hide weak delivery behind a long tool list or a workflow tool such as Metaflow.
Scale and adoption add CI, repository structure, parameterization, and tests. They also add data versioning, traceability, and experiment capture [4]. The portfolio version should expose those same checkpoints even if it uses a local dataset snapshot rather than a full platform. When teams reuse the same project standard, MLOps Adoption at Scale shows how those checkpoints turn into platform rollout and team ownership.
Reproducible Training
Reproducible training starts with a visible run path. Include a training command, configuration file, and dependency lock or environment file. Also include the data reference, run parameters, metric output, and saved artifact. For Reproducibility, a data snapshot, hash, or manifest can be enough when it lets another person rerun the training job and compare the result.
This bar rests on repository structure, tests, data traceability, and experiment capture [4]. The same project structure moves notebook code into packages and CI/CD [3].
Experiment Records and Registry Handoff
Track at least one baseline run and one improved run. Each run should store the dataset reference and parameters. It should also store metric values and the artifact path. Keep failure notes with the run record before promoting one artifact with a registry record.
The handoff from experimentation to deployment should be explicit. Link experiment tracking to the model registry so the registry becomes a release boundary rather than a storage folder. That release boundary is one concrete checkpoint in the Notebook to Production Workflow [1].
A simple interim registry is an acceptable lightweight version [5]. The record still needs model and data versions. It also needs the environment, evaluation result, approval state, and deployment target. In a portfolio project, a table or YAML manifest can satisfy that requirement when it gives reviewers the exact artifact and approval state.
Deployment Boundary
Show either batch scoring or online serving. Batch scoring can write predictions to a table, while online serving can be a small API. The project should include input validation, output schema, logs, and one fallback rule. Batch and online deployment are separate modes [1], so the README should name which serving mode it implements and why.
Simple, maintainable systems with modular, testable code are the priority. Production ML capstones include tests, monitoring, A/B testing, and CI/CD [6]. When the project also has to support interview prep, connect those deployment choices to a machine learning system design interview answer. Use CI/CD and Production when the project needs a release note, a deployment command, or a rollback path.
Monitoring, Incidents, and Feedback
Monitoring should cover service health, input quality, and prediction distributions. It should also cover business outcomes and name upstream causes that could break the model. model monitoring connects to upstream ETL and data pipeline causes. That makes data profiling and root-cause visibility part of the project rather than an optional dashboard [7]. Use Model Monitoring vs Data Observability when the checklist needs to separate model-behavior alerts from freshness, schema, lineage, and other upstream data signals.
Business value and incident readiness start from business KPIs and add incident prep, postmortems, and live test sets [8].
Input shifts, unit changes, and feature drift are monitoring concerns, and logging and reproducibility become monitoring concerns too [8]. Use Evaluation for metric choices and Model Monitoring for the model-specific signals.
Feature Reliability
Feature-heavy projects should address training-serving consistency, feature validation, and ownership, and review drift and served-feature logs. Feature Stores frame that online-offline feature path when a project needs one. Feature responsibilities, validation, ownership, and governance ground that work [9].
If the project uses a feature table, the README should state who owns each feature and how training data maps to served inputs. It should also name the drift or freshness check that would alert the owner.
Production Project Evidence
A production ML portfolio project is ready for review when it includes:
- a problem statement, baseline, and metric tied to Machine Learning Portfolio Projects and Evaluation
- a reproducible training command, configuration, dependency record, data reference, and saved artifact
- at least two tracked runs with parameters, metrics, artifact paths, and failure notes
- a registry or manifest entry with model version, data version, environment, evaluation result, approval state, and deployment target
- a batch scoring job or online API with input validation, output schema, logs, and fallback behavior
- monitoring signals for service health, input quality, prediction behavior, business outcomes, and upstream data causes
- a rollback or retraining rule with the owner action that follows an incident
The list condenses lifecycle checkpoints from production ML discussions. Guests connect those checkpoints to reproducibility, deployment handoff, monitoring, and rollback [1] [3] [8].
The surrounding topic pages cover each piece of the project: