Industrial AI Governance & MLOps worked example
Training Data Volume at 99% data capture uptime: a worked example
What does the result look like when data capture uptime reaches 99%? The full calculation is worked below with real intermediate numbers. Use it when a data scientist or plant engineer needs to know whether a data collection plan can supply enough usable samples for model training or validation.
The inputs for this scenario
- Training records per collection cycle: 250 records / cycle (unchanged)
- Planned data collection cycles: 120 cycles (unchanged)
- Data capture uptime: 99 % (raised for this scenario; the documented default is 92)
- Usable data quality yield: 85 % (unchanged)
Working through the calculation
- Applying the documented formula (Gross training data volume = training records per collection cycle × planned data collection cycles) to the inputs above produces each figure below.
- At this operating point the engine returns 25,245 records for usable training data volume, the number this scenario is built around.
- At this operating point the engine returns 30,000 records for gross training data volume.
- At this operating point the engine returns 300 records for data capture uptime loss.
- At this operating point the engine returns 4,455 records for data quality yield loss.
How this compares with the baseline
- Against the tool's baseline example, where data capture uptime sits at 92% and the headline result is 23,460 records, this scenario comes in 7.61% above the baseline at 25,245 records.
- A figure at this level is achievable when data capture uptime is genuinely sustained, not just peaked for a shift. It assumes uptime and quality yield are stable averages; bursty outages or a labeling rule change mid-campaign will shift the real usable count.
Results at a glance
- Usable training data volume: 25,245 records (headline result)
- Gross training data volume: 30,000 records
- Data capture uptime loss: 300 records
- Data quality yield loss: 4,455 records
Run it with your numbers
- Every input above is editable in the live Training Data Volume calculator, which recalculates instantly and can be shared with the inputs intact.
Last reviewed 2026-05-12.