A machine-readable medium may store instructions execut able by a processing resource to
access log data of an enterprise and extract time-series data of an enterprise entity from the …

In an example embodiment, clusters of nodes in a network are monitored. Then the
monitored data may be stored in an open time-series database. Data from the open time …

A device may receive one or more data models that have been trained on a set of historical
network performance indicators. The set of historical network performance indicators may …

Described is an approach for performing context-aware prognoses in machine learning
systems. The approach harnesses streams of detailed data collected from a monitored target …

A model training service of a provider network receives data from edge devices of a remote
network. The model training service analyzes the received data. The model training service …

Techniques are described herein for estimating CPU, memory, and I/O utilization for a
workload via out-of-band sensor readings using a machine learning model. The framework …

Embodiments use a hierarchy of machine learning models to predict datacenter behavior at
multiple hardware levels of a datacenter without accessing operating system generated …

Described is an approach that provides an adaptive solution to missing data for machine
learning systems. A gradient solution is provided that is attentive to imputation needs at each …

In one embodiment, a device in a network maintains a plurality of anomaly detection models
for different sets of aggregated traffic data regarding traffic in the network. The device …

In one embodiment, a learning data processor determines a plurality of machine learning
features in a computer network to collect. Upon receiving data corresponding to the plural ity …