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Automate external data discovery and enrichment for more accurate predictive models.
Data scientists are painfully familiar with the frustration of great machine learning models that were doomed to be stuck in the POC stage. It’s no secret that while most organizations understand the importance of machine learning models, most initiatives never make it off the ground, or produce the impact they were designed to provide.
How can you avoid this fate, and push your machine learning models all the way to deployment? It’s all about understanding the pitfalls that await most ML initiatives and planning to avoid them. It’s about rethinking the development cycle, and understanding that you need to plan for tomorrow, not today.
This guide 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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