8 Ways SQLstream Rocks Machine Learning

We’ve repeatedly heard of the value we bring to machine learning from customers and prospects. That’s because Guavus SQLstream uses the power of SQL to enable real-time delivery of machine learning over streaming data at a latency, performance and convenience that nobody else can match. 

Here are 8 ways we make it possible:

  1. We provide all of the data discovery and wrangling. The StreamLab visual development environment transforms raw data into groomed streams using the full power of parallel dataflow-based SQL and time series analytics, augmented with location-based data and historical enrichment.
  1. We enable the continuous training of machine learning models. By creating and combining streaming views of data, we can continuously feed these back into the machine learning training module.
  1. We can dynamically update models with zero interruption in service. Now you can update existing predictive and classification pipelines without the processing skipping a beat.
  1. We provide distributed plumbing. With federated and clustered server options and distributed agents, SQLstream enables the most diverse and distributed data collection deployment scenarios
  1. We support best of breed machine learning capabilities, including open source (Apache SystemML), supervised learning (DataRobot) and unsupervised learning (Anodot) and other 3rd party machine learning providers.
  1. We provide the lowest latency scoring and highest throughput – over 32k/records per second on a commodity 4 core server, latency less than 1ms.
  1. We support the widest range of data formats. The key to machine learning is to feed it columnar data which can be difficult without a good data wrangler. SQLstream’s StreamLab discovers the fields for machine learning from any format of data including: JSON, XML, CSV, AVRO and Google Protobuf.
  1. We enable the outputs and actions triggered by machine learning decisions. When the right conditions are met, Guavus SQLstream can send alerts, take actions (like freeze a suspect account), update dashboards and output data to new streams or populate historical data stores.

So, if you’re looking towards machine learning to increase the predictive power and accuracy of your stream analysis, there is no better streaming analytics platform to run your continuously updated machine learning models.

Want to get started on your machine learning project? Give us a call or download our community edition to get started.