Nlyte Machine Learning

Predict the Future with Our Premier Machine Learning Engine

Nlyte Machine Learning, powered by IBM Watson IoT, combines Nlyte’s industry leading solution for managing data center energy infrastructure with the premier machine learning engine of IBM Watson IoT. Tightly integrated, the two enable organizations to rapidly capture, normalize, and analyze large amounts of data to optimize their data center operations and head-off potential issues and outages before they happen.

Why Machine Learning from Nlyte?

Nlyte Machine Learning Powered by IBM Watson IoT is a purpose-built framework, developed by the two leaders in AI and Data Center Management, IBM and Nlyte. It addresses scale, complexity, and optimization requirements of modern data centers. Nlyte Machine Learning is easy to adopt leveraging data already generated by sensors, critical infrastructure, IT equipment, and applications. There is a complete framework including data collection, patterns for predictive power and thermal, command and control actions from analytics reporting, as well as tooling for custom pattern development.

Benefits of Nlyte Machine Learning

  • Risk avoidance
  • Overhead reduction
  • Performance optimization
  • Improve maintenance strategies

Key Use Cases

Predictive Power and Thermal
Workload/Hybrid Compute Optimization
Placement Optimization
Multi-variate Maintenance and Failure Prediction
Alarm and Alert Management
  • Understand patterns that allow you to raise and lower data center temperatures
  • Plan future power needs in advance with accuracy
  • Improve density

 

  • Improve power usage effectiveness (PUE) and reduce carbon footprint
  • Identify optimum compute environments for application workloads
  • Plan dynamic workload migrations and repatriations
  • Install equipment based on thermal, power, communications, and application vectors
  • Improve space and power efficiency
  • Build out a future-ready compute infrastructure
  • Granular information for component detail and application history
  • Enhance preventative maintenance routines
  • Refine failure prediction
  • Intelligently filter alarms and alerts
  • Prioritize significant events, either standalone or in complex chains