network model

How to use Machine Learning for Testing and Implementing Optical Networks

How to use Machine Learning for Testing and Implementing Optical Networks

January 22, 2020

Optical transport networks are evolving to have unprecedented flexibility with advances such as finely tunable bitrate transponders, allowing dynamically-adaptive …
Container & Kubernetes Networking 101

Container & Kubernetes Networking 101

January 19, 2020

With the widespread adoption of Docker, containers have taken the DevOps world by storm. Cloud-native is the new programming paradigm. Since developers are not only the creators of these systems, but …
Cognitive Workloads on the Cloud: What Clouds Need To Do

Cognitive Workloads on the Cloud: What Clouds Need To Do

January 18, 2020

With the emergence of new powerful hardware accelerators, such as Graphical Processing Units (GPUs) and Programmable Field Gate Arrays (FPGAs), more and more artificial intelligence (AI) has appeared …
Securely building deep learning models for digital health data

Securely building deep learning models for digital health data

December 30, 2019

Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks.
The over-under on container networking

The over-under on container networking

November 20, 2019

The time has come for network engineers and operators to prepare for the unique challenges brought on by cloud-native applications. Lee Calcote walks you through the different types of container connectivity options available, exploring their specific function (when they should be used) and comparing their performance characteristics.