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ML Fundamentals Checklist

This checklist helps ensure that our ML projects meet our ML Fundamentals. The items below are not sequential, but rather organized by different parts of an ML project.

Data Quality and Governance

  • There is access to data.
  • Labels exist for dataset of interest.
  • Data quality evaluation.
  • Able to track data lineage.
  • Understanding of where the data is coming from and any policies related to data access.
  • Gather Security and Compliance requirements.

Feasibility Study

  • A feasibility study was performed to assess if the data supports the proposed tasks.
  • Rigorous Exploratory data analysis was performed (including analysis of data distribution).
  • Hypotheses were tested producing sufficient evidence to either support or reject that an ML approach is feasible to solve the problem.
  • ROI estimation and risk analysis was performed for the project.
  • ML outputs/assets can be integrated within the production system.
  • Recommendations on how to proceed have been documented.

Evaluation and Metrics

  • Clear definition of how performance will be measured.
  • The evaluation metrics are somewhat connected to the success criteria.
  • The metrics can be calculated with the datasets available.
  • Evaluation flow can be applied to all versions of the model.
  • Evaluation code is unit-tested and reviewed by all team members.
  • Evaluation flow facilitates further results and error analysis.

Model Baseline

Experimentation setup

  • Well-defined train/test dataset with labels.
  • Reproducible and logged experiments in an environment accessible by all data scientists to quickly iterate.
  • Defined experiments/hypothesis to test.
  • Results of experiments are documented.
  • Model hyper parameters are tuned systematically.
  • Same performance evaluation metrics and consistent datasets are used when comparing candidate models.

Production

  • Model readiness checklist reviewed.
  • Model reviews were performed (covering model debugging, reviews of training and evaluation approaches, model performance).
  • Data pipeline for inferencing, including an end-to-end tests.
  • SLAs requirements for models are gathered and documented.
  • Monitoring of data feeds and model output.
  • Ensure consistent schema is used across the system with expected input/output defined for each component of the pipelines (data processing as well as models).
  • Responsible AI reviewed.