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:
- The common pitfalls when planning a new ML project and how to avoid them
- How to plan for models that will be scalable and adaptable
- Four steps you can take to start building models that will go from theory to deployment faster