With the ability to process up to 2.5 billion transactions per day, IBM z Systems mainframes are data-generating powerhouses: that’s why they’re favoured by large-scale enterprises. But massive quantities of data without intelligent analysis is the equivalent of speed without control.

Until now, companies that wanted to leverage the powerful analytics of IBM Watson had to first make the decision to move their data off-premise. Now, with IBM’s Machine Learning Platform, that compromise has been eliminated: all the necessary resources reside in the private cloud.

With IBM Machine Learning, data scientists can automate the creation, training, and deployment of operational analytic models that will support:

• Any language (e.g., Scala, Java, Python)
• Popular Machine Learning frameworks such as  Apache SparkML, TensorFlow, and H2O
• Any transactional data type

Gone is the cost, latency, and any risk of moving data off-premise.