Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects.

Timothy Spann leads a hands-on deep dive into using Apache MiniFi with Apache MXNet and other deep learning libraries on edge devices.

Deep Learning (DL) is a computational intense form of machine learning that has revolutionize many fields including computer vision, automated speech recognition, natural language processing and artif …

In the past, you needed a high-end proprietary stack for advanced machine learning, but today, you can use open source machine learning and deep learning algorithms available with distributed computing technologies like Apache Spark and GPUs. Nanda Vijaydev and Thomas Phelan demonstrate how to deploy a TensorFlow and Spark with NVIDIA CUDA stack on Docker containers in a multitenant environment.

ML researchers spend too much time building infrastructure to support their work. Kubeflow aims to solve that by using Kubernetes to build an open, scalable, and extensible platform for ML. Since our …

Deep learning model performance relies on underlying data. Dong Meng offers an overview of a converged data platform that serves as a data infrastructure, providing a distributed filesystem, key-value storage and streams, and Kubernetes as orchestration layer to manage containers to train and deploy deep learning models using GPU clusters.

TLDR: Kubernetes modern production patterns for Deep Learning applications and a deep dive into the Kubernetes GPU and Hardware accelerators subsystem and its challenges (performance, scheduling, mon …

Kubernetes has seen broad interest from the machine learning community and many users are bringing GPUs to their clusters. However, compiling, installing, and updating the NVIDIA kernel modules needed …