We’d like to inform you about the deprecation of the Watson Machine Learning JSON Token Authentication service. This method of authentication will be retired on May 30, 2019.
This post is an excerpt from our solution tutorial that walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.
Many organizations have started to explore the value that machine learning can bring—from illuminating previously “dark data” such as images and videos, to creating models that help to guide or even automate business decision-making. However, very few companies have gone beyond pilots and prototypes, or made the transition from one-off projects to a scalable, repeatable workflow. Too often, machine learning exists in a bubble of its own, instead of being understood in the context of the broader data science workflow.
Machine learning is one of the most exciting areas of data science, with enormous potential to transform data into the pure gold of competitive advantage. Data scientists can seem like wizards when their models first accurately predict customer or market behavior, or reveal valuable insight from previously untapped data sources.
Last year we made data science a team sport with IBM Data Science Experience, our award-winning IDE for analytics. This summer we brought to market IBM Watson Machine Learning that allows companies to put models into production with easy model management and full workflow automation. And last week, we announced we've grown up those two products into Watson Data Platform, while adding new features.