HyperSense AI
Distributed Computing

Build models faster on a large volume of data and trains the machine learning algorithm at scale by leveraging distributed computing with HyperSense AI.

Challenges with Traditional Systems

Inability to handle a large volume of data

Need for managing the compute power of models

Need for better response times from the models

Limitation of ML models in terms of scalability

Challenges

Model training becomes 9x faster

HyperSense AI leverages scalable, distributed multi-mode machine learning to handle enormous data to make informed decisions that improve performance and increase accuracy. The presence of multiple CPUs trains models faster, and allocating large-scale learning processes onto several workstations enables faster learning algorithms and response times. Cloud resource allocations on HyperSense AI are handled with the Kubernetes-based deployment approach.

Benefits of Distributed Computing

Faster training of data

Trains a vast amount of data faster than traditional system to develop efficient machine learning algorithms and with better response times.

Quick and better decisions

Analyses from a large amount of data reduce errors made by the machine and assist in making informed decisions quickly.

Increase accuracy

Improve performance, efficiency, accuracy, and scale with multi-node Machine Learning algorithms to larger input data size.

Scale huge volume of data with Distributed Computing

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