How Machine Learning Models Improve Over Time

It’s all about the training data.

Machine learning our automated machine learning (autoML) models improve over time. You’ve probably read this in an article or heard someone say a version of this fact at a conference or on a podcast. For example, Jonathan Corbin, the VP of Global Strategy and Success at HubSpot recently said the following about no-code AI and data science,

“We’ve seen positive movement from the models we’ve put in place and out the output from them. I think the important thing to remember with ML and self-learning models is that they get better over time. And so, as you’re thinking about implementing a solution using AI, ML, and no-code, I would say you commit to a goal, and you invest in the technology that helps you get closer to that endpoint.”

But, how does it work? How are machine learning models able to improve over time?

When using a machine learning model, you have two sets of data: historical training data and production data. The historical data is what the model learns from by detecting patterns in the data that led to the outcome. For example, all the data around the customers who have already churned or the ones you were able to upsell.

The production data is a new file of active customers; it contains all the same information as the historical, except the result. That is what the model will predict by scoring or forecasting. For example, who is at risk of churn next quarter, or who is likely to buy the next level of your product.

When you run your first model, you have a set amount of historical data to train your model, but that data grows over time as your business continues to run. When the ‘new’ historical data gets added to the model, that model will improve its learning and increase the accuracy of the predicted outcomes. The model’s accuracy can improve every time data gets added to the training file. Squark has some very innovative and cutting-edge ways to help you maintain your models and results.

Streamlining the process

Once upon a time, this was a complicated process to execute, but technological advances are simplifying it. AutoML was a fundamental shift in the machine learning process, making it easier to build and use machine models. However, in many instances, autoML still requires the skills of a data scientist to execute.

Today, no-code AI solutions, like Squark, go way beyond traditional autoML and have transformed the entire process. Analysts and business users can easily produce accurate, reoccurring data science projects in just clicks. Updating models as data changes is simple with Squark. Using Squark’s scheduler (part of our intelligent connectors, you can schedule training (and retraining), scoring (and rescoring) the production data, and the exporting of model results to other systems. By automating regular updates on a regular schedule, you can be confident that you’re actioning off the most accurate and timely results possible.

In the most premium version of Squark, users can automatically profile and identify data drift, and learn when to update learning, scoring, exporting of results and models in even the most complex big data environments.

The takeaway: Whether you’re a data scientist, an analyst, or a business user, remember to update your historical/training and not just your production data to improve the accuracy of the predicted outcomes.

Interested in learning more? Contact us here to review Squark’s platform.