We're thrilled to announce we are launching Analyzr, our service streamlining machine learning analytics for sales and marketing teams!
Machine learning is undeniably useful. ML simplifies the computation of complex data outputs and extracts value from data that humans simply can’t achieve. However, just because a machine is executing the heavy lifting, doesn’t mean ML models are a “set-it and forget-it” type of activity. Like most things in life, data changes over time. Relationships between variables in the data pipeline can also change. These changes prompt what is called Model Drift, sometimes also referred to as Model Decay.
ML models don’t like change. They are trained to assume future data ingested will look like the data used to build the model, so Model Drift is a thing we want to monitor for and prevent. In the subsequent post, we’ll describe two types of Model Drift and outline a few ways you can identify and take action to prevent model degradation.
Machine learning is a better prediction technology, and with better predictions you can more easily optimize business outcomes. However, for many business users, the idea of implementing this feels unattainable. At G2M Insights we know that leveraging machine learning is possible for every company, no matter the size or maturity of the business. We know, because for the past several years the G2M team has been living this day-in and day-out, supporting our clients with building, training, and implementing unique-to-their-business analytic models in areas like propensity modeling and clustering. We’ve learned a few things along the way, and wanted to share some of those lessons with you.
