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Automate external data discovery and enrichment for more accurate predictive models.
Machine learning has gone from buzzword to business necessity, and implementing it is quickly becoming mandatory. However, it can be easier said than done if you don’t have a foundation or a strong data science team already in hand to help the process along.
How can you make sure you’re building your machine learning infrastructure the right way? You’d be surprised to learn that you don’t need to have a full data science staff at hand. By looking within your organization, you can quickly start building a machine learning capacity that’ll give your teams a boost.
This whitepaper discusses:
Automate external data discovery and enrichment for more accurate predictive models.
It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.
This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.
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