How To Accelerate Machine Learning With Data Compression Algorithm For A Distributed Ecosystem

The pipeline of a machine learning project consists of various stages with each stage having its own fair share of significance in influencing the outcome or some prediction. The changes that are made to these components of a pipeline say, during its training, computations can be performed locally. This update has to be transmitted so that every training step incorporates it. At the fundamental level, the data that is being transmitted during the training step, also plays a key role in the outcome. The data bandwidth that needs to be transferred and the associated accuracy of the model. And that’s why the ML team has to walk a tightrope when it comes to maintaining accuracy with torrential inflows of data. Whenever a model is deployed for training, initially there is a forward pass