Loud ML is a highly scalable machine learning API. It allows you to analyze big volumes of data quickly and use machine learning models in your application without the algorithms complexity. It is generally used as the underlying engine/technology that powers applications that have predictive requirements.
Here are a few sample use-cases that Loud ML could be used for:
- dynamically scale and drive smart load-balancing decisions for VMWARE and/or KVM virtual resources according to predicted load, enabling cloud and hosting providers to reduce operating costs while delivering high-quality services and response times to their users;
- embed smart intent-based decisions in network services and NGN equipment when network traffic patterns indicate that streaming congestion will impact customer experience, permitting service providers to scale and reduce operating costs;
- spot anomalies in e-commerce purchasing patterns, and automatically send smart alerts which filter out noise, ensuring e-commerce companies receive the most relevant alerts when things go wrong in the customer’s journey; and,
- predict changes in customer purchasing history, enabling retail companies to forecast demand with optimal accuracy.
For the rest of this tutorial, you will be guided through the process of getting Loud ML up and running, taking a peek inside it, and performing basic operations like creating, training, and using your data to get accurate predictions. At the end of this tutorial, you should have a good idea of what Loud ML is, how it works, and hopefully be inspired to see how you can use it to build sophisticated applications mining intelligence from your data.