Loud ML is a new deep learning API that makes it simple to prepare, train, and deploy machine learning models for predictive analytics with your favorite database. The user selects the times series that they want to model and sets the model date ranges, then Loud ML will build the models and save them back into the desired database for analysis. Loud ML does all the work and removes the complexity of machine learning with Tensorflow.
It is used as the underlying technology that powers applications with predictive capabilities, and shortest time to market.
Here are a few sample use-cases that Loud ML is used for:
- Detecting abnormal dips in user traffic and responding to incidents before they impact customers satisfaction
- Detecting outliers in periodic fluctuations over changing baseline; eg., e-commerce transactions where the time of day, and even the season, implies varying changes in the data
- Spotting abnormal load in a distributed database
- Dynamically spotting network traffic patterns and anticipating congestion before it impacts customer experience
- Forecasting various quantities: for finance, for retail, for energy, and for supply chain optimization
- Abnormal fraud pattern detection
- Wizard magic, to reveal hidden features; eg, guessing footsteps when your only data is X,Y,Z acceleration.
Wizard magic is powered by
supervised learning. All other cases are fully
since they do not require labelled data to forecast the output.
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 that mine intelligence from your data.