Webinar: Intel AI Presents Distributed Deep Learning Using Horovod

Key takeaways

Deep learning requires large datasets and significant computational resources for effective implementation.

Distributing computational needs across multiple sources can optimise performance for large-scale deep learning tasks.

Horovod is a TensorFlow library that facilitates the reliable distribution of compute resources in deep learning.

Data scientists must stay informed about advancements like multi-node clusters to remain competitive in the field.

Intel's webinar aims to equip ML/AI professionals with the necessary knowledge to navigate evolving computing landscapes.

Deep Learning is one of the most data-intensive tasks out there, owing to its requirement of large datasets. In addition to this, it is extremely hungry in terms of computational resources, requiring hardware acceleration and lots of processing power. To register for the webinar, click here. To optimise this, many take the path of distributing the model's computational needs over different compute sources. In the case of a large-scale deep learning implementation, there is a need for dedicated high-performance compute architecture to satisfy the compute needs of the model. However, it is not easy to distribute such a complex and varied process over high-performance compute scale infrastructures. This can be remedied with the use of solutions such as Horovod, a TensorFlow library which allows for dependable distribution of compute resources in deep learning. To register for the webinar, click here. With the rise of multi-node clusters for training deep learning models, data scientists must keep up-to-date on such advancements so as to not be left behind the curve. Keeping this in mind, Intel has organised a webinar targeted at ML/AI professionals. By giving them the knowledge they need to handle the shifting landscape of computing, Intel's webinar will equip data scientists for the future of deep learning. Titled 'Distributed Deep Learning using Horovod', the webinar will be conducted by Jyotsna Khemka, a Senior Technical Consulting Engineer at Intel. To register for the webinar, click here. Who Should Attend:

Data Scientists looking to optimize their deep learning workloads
ML and AI enthusiasts who can supercharge their data science activities using the technology
Industry players looking to improve their bottom line using cutting-edge technology
Job seekers looking to add skills to their resume to improve hireability

When: Thursday, July 18th, 11 AM - 12 noon. To register for the webinar, click here.

Check more about AI at Intel, here.

Webinar: Intel AI Presents Distributed Deep Learning Using Horovod

Key takeaways

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