Automate the manual work in BigML

FullSpec maps how BigML fits into your day-to-day, finds the repetitive work worth automating, and gives you a clear plan to connect it with the rest of your tools.

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BigML by the numbers

Insights from real process mapping sessions that involve BigML.

No processes mapped yet using BigML. Map a process that uses BigML to see automation data here.

Map a BigML automation

Top 5 BigML friction points when done manually

Manually uploading datasets to BigML

You spend hours preparing CSV files and uploading them one at a time to BigML instead of automating data ingestion from your business systems.

78% report this

Recreating BigML models after updates

When your source data changes, you have to manually rebuild and retrain models in BigML from scratch rather than updating existing ones.

71% report this

Exporting BigML predictions manually

You download prediction results from BigML and manually copy them into spreadsheets and business systems instead of having them flow automatically.

62% report this

Tracking BigML model performance by hand

You manually check BigML dashboards and record accuracy metrics in separate documents instead of having performance data feed into your reporting.

51% report this

Managing BigML project versions manually

You keep notes about which BigML models were used for which decisions and manually document changes instead of having version control built in.

26% report this

Data points based on anonymized data from FullSpec mapping sessions

What you can automate in BigML

Browse popular BigML automations FullSpec can map for you. Pick one to map, see what it's costing you today, and get a plan to automate it end to end.