We know how hard it is to prepare training data for machine learning projects. We’ve been labeling and annotating training datasets for enterprise data science teams for years.
Many, if not most, of our clients tried and failed to tackle the training data challenge on their own. Their stories are all quite similar: they found the volume of data and the complexity of the process to be overwhelming, something they were not ready for. Invariably they burned through a greater-than-expected percentage of their budget, fell behind schedule, and were at risk of failing to deliver a production-ready model.
For teams that are considering DIY training data we have created a downloadable blueprint. It lays out the tools, people and skills required to prepare enterprise ML training datasets, and includes a pre-flight checklist that will help you to answer important questions like:
Do you have access to a workforce that can label and annotate data at scale?
Does your team bring all the necessary skills to training data preparation?
Do you have the specialized project management and data labeling tools you’ll need?
The blueprint may end up becoming a real checklist for you. It may also persuade you that you aren’t and shouldn’t be in the training data business. Either way, it delivers value.