Alegion is in the business of preparing machine learning training datasets for Fortune 1000 organizations. When the team at Alegion first meets with a prospective client, it's pretty predictable. Most of the time the meeting is with the data science team responsible for the organization’s first serious machine learning project.
They tell Alegion that:
- Their project is potentially strategic and highly visible.
- The board is paying attention.
- Their internal proof of concept – a mix of home-grown and off the-shelf algorithms and training data – went well.
- Now they are trying to get the model to a level of confidence that will let them to put it into production.
- They’re preparing the training dataset themselves, and the volume of data and the complexity of the process has become overwhelming.
- As a result, they’ve burned through a greater-than-expected percentage of their budget, they’re behind schedule, and their algorithm is far from being production-ready.
If this describes your situation, this guide from Alegion may be for you.
Think of it as a pre-flight checklist for data science teams that are contemplating preparing their own training data. As Alegion shares with you the capabilities you’ll need, use this checklist to measure your own level of preparedness.
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