PyTorch Lightning enables the usage of multiple GPUs to accelerate the training process. It uses various stratergies accordingly to accelerate training process.
Microsoft has formulated a library named DoWhy to build efficient and robust causal models for critical domains.
Pytorch has formulated a framework named TorchRec sharding that is suitable for training and modelling large scale recommendation systems.
Pytorch learning rate scheduler is used to find the optimal learning rate for various models by conisdering the model architecture and parameters.
The Bonsai Brain is a low code AI component that is integrated with Automation systems. The Bonsai Brain focuses on adding value to various Autonomous and AI systems.
MLCube is an interface which facilitates easy model sharing across platforms and account for increased model usage across platforms.
XLA is a compiler used to accelerate training time of Tensorflow models and reduce memory consumption.
This article mainly focuses on the Tensor2Tensor library and to understand the dynamic abilities to handle and process complex models.
Tensorflow model remediation is a framework used to obtain fairness free and bias free models. It aims to produce robust models that is not affected by sensitive attributes of data.
The weight clustering API is one of the use cases of the Tensorflow model optimization library and it aims to optimize the models developed so that they can be easily integrated into edge devices.
The MLOps maturity model is a key component of the MLOps.This article aims to explain the MLOps maturity model and its importance in the production environment.
Any machine learning classification model is evaluated on error parameters to understand the models working in real-time. The main aim of developing a model is
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