In reality, the machine learning flow is more cyclical: Data comes in, it is used to train a model, and then the accuracy of that model is assessed and the model is retrained as new data arrives ...

While teams spend lots of energy developing a machine learning model, it’s hard to actually deploy the model for customers to use. That’s where Graphpipe comes in.

• Data Acquisition: Gather comprehensive customer data from various sources. • Model Building: Develop ML models tailored to your needs, train them with historical data, and fine-tune for ...

Also read: Alteryx expands product set, makes data science acquisition A recurring problem By now, the challenges of operationalizing machine learning models are starting to become better understood.

Old-school machine learning might not have the allure of the latest AI trends, but it has consistently proven its worth.