Plan an ML project around data access, labeling and review work, measured training throughput and explicit evaluation and deployment gates.
Write the user or business decision the model will support, the deployment context and the cost of relevant errors. Choose a baseline and evaluation criteria before model iteration. Define the unit of observation, data time boundaries and how train, validation and final evaluation data will be separated to avoid leakage.
Confirm lawful access, permissions, coverage, label definitions and whether the data represents the intended use. Inspect a sample and run a labeling pilot with an explicit review protocol. Count source items, assignments per item and review events, then use measured rates and working time in the labeling budget. Assign owners to access or quality blockers.
Implement the simplest useful baseline with versioned data, code and configuration. Record preprocessing, evaluation and experiment outputs so another run can be compared. Measure completed samples over a synchronized elapsed interval. Use the serial pipeline model only when stage timings do not overlap; worker/prefetch behavior requires end-to-end measurement.
Record dataset size, complete epochs, logical batch size and the actual incomplete-batch policy. Use observed training samples per second from the compatible model, precision, hardware and data path. Add validation, checkpoint and startup overhead once. Budget the planned experiments, retries and queue waits separately; one run’s arithmetic is not the entire modeling phase.
Combine data work, labeling/review, baseline development, evaluation, integration and operating tasks with owners. Compare working hours with actual team availability, and list dependencies and waiting periods separately. Require evaluation evidence before a deployment decision, including relevant error slices, human fallback, monitoring and rollback. Avoid assigning universal percentages of the project to data or modeling.
Compare the baseline and candidate against the predeclared evaluation criteria and practical costs. Preserve a final evaluation set for the intended final assessment and record limitations without relabeling repeated tuning as independent validation. Revise remaining work from actual runs and labeling throughput. Download the plan, cost assumptions, version references and go/no-go criteria for the next review.
Draft and download the data, experiment and delivery decision record.
Price explicit label assignments, review events and setup while summing working hours.
Calculate serial-stage or observed end-to-end sample throughput under the correct measurement scope.
Estimate a compatible training workload with an explicit incomplete-batch policy and overhead.
Compare remaining project work with real team capacity or comparable team throughput.
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PyTorch documents incomplete-batch handling, parallel workers and distributed sampler behavior. Check the actual loader; a simple logical-dataset model does not reproduce every sampler configuration.
Keep these task-specific records with the tested version and review date.
| Record | Include | Verify |
|---|---|---|
| Labeling | Assignments, review events, rates and working minutes | Pilot supports the entered workload assumptions |
| Compute | Processed samples, compatible rate and extra overhead | Actual run log confirms the scope |
| Schedule | Owners, availability, dependencies and decision gates | Working hours are separate from waiting time |
Keep these task-specific records with the tested version and review date.
| Record | Include | Verify |
|---|---|---|
| Reproducibility | Data/code/config versions and baseline | Comparable run can be repeated |
| Evaluation | Split boundary, metric, slices and uncertainty | No leakage or hidden repeated tuning |
| Operation | Monitoring, fallback and rollback owner | Deployment decision has explicit criteria |
A small working pipeline reveals data and integration work that a phase percentage cannot capture.
Training duration is only one part of a project that also needs data, evaluation, integration and operating work.