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IBM: 'Deep Learning as a Service' Now Available
IBM has announced it is now offering Deep Learning as a Service ('DLaaS") via IBM Watson Studio.
The services let data scientists "visually design their neural networks and scale out their training runs while auto-allocation means paying only for the resources utilized," the company explained.
Through Watson Studio, DLaaS comes with a cloud-native set of tools that provide an environment for building, training and deploying AI models that work with various types of data.
IBM's new offering uses the RESTful API approach, following the familiar "as-as-service" mechanism to ease the burden of refining data, training neural network models and creating deep learning models.
"Drawing from advances made at IBM Research, Deep Learning as a Service enables organizations to overcome the common barriers to deep learning deployment: skills, standardization, and complexity," IBM said in a post. "It embraces a wide array of popular open source frameworks like TensorFlow, Caffe, PyTorch and others, and offers them truly as a cloud-native service on IBM Cloud/"
"Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework," IBM said. "The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes. A resource-provisioning layer enables flexible job management on heterogeneous resources, such as GPUs and CPUs on top of Kubernetes."
IBM also said it's open sourcing a core component of the new service, called the Fabric for Deep Learning (or FfDL, pronounced "fiddle"), which is now on GitHub.
Jim Zemlin, executive director of The Linux Foundation, weighed in on IBM's open sourcing of FfDL.
"Just as The Linux Foundation worked with IBM, Google, Red Hat and others to establish the open governance community for Kubernetes with the Cloud Native Computing Foundation, we see IBM's release of Fabric for Deep Learning, or FfDL, as an opportunity to work with the open source community to align related open source projects, taking one more step toward making deep learning accessible," Zemlin said. "We think its origin as an IBM product will appeal to open source developers and enterprise end users."
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David Ramel is an editor and writer at Converge 360.