We have renamed RStudio Server Pro to RStudio Workbench. This change reflects the product's growing support for a wide range of different development environments. RStudio Workbench enables R and Python data scientists to use their preferred IDE in a secure, scalable, and collaborative environment–whether that is the RStudio IDE, JupyterLab, Jupyter Notebooks, or VS Code. We want RStudio Workbench to be the best single platform to support open source, code-first data science, whether your team is using R or Python. If you'd like to learn more about the reasons behind this name change, and what it might mean for you, please check out our FAQ here, or set up a conversation with your customer success representative.
As we have previously described, DL Workbench is a tool that allows you to import Deep Learning models, evaluate their performance and accuracy, and perform different optimization tasks, like calibration for 8-bit integer inference. Profiling and model optimization are device-specific, therefore, to achieve maximum performance in a deployment environment, we need to perform these steps directly in that environment. DL Workbench helps you with accessing those capabilities on remote machines. Please note that if you want to have access to numerous hardware configurations ready for work and you do not have them locally or in your private lab, you can run DL Workbench in the Intel DevCloud for the Edge, where you can easily start experiments with available hardware. In this paper, we primarily focus on the case when you prepare the model for deployment and need to benchmark it on a specific hardware setup available in your private lab or a pre-production sandbox.
In the last two columns, I explored the features and services provided by Azure Machine Learning Studio. In September 2017, Microsoft announced a new suite of tools for doing machine learning (ML) on Azure. The cornerstone of these new tools is Azure Machine Learning Workbench. However, what could be better for doing ML than the simple drag-and-drop interface of Machine Learning Studio? Machine Learning Studio is an ideal tool for creating ML models without having to write code, but it falls short in several areas.
Introduction It's early morning and the sun is shining, but where is the birdsong? The new bird feeder should be filled with seeds, but it's empty, and a happy squirrel is scurrying up a nearby tree with the stolen goods. Unfortunately, most modern bird feeders have not been able to prevent this common problem. By bringing the bird feeder into the 21st century, we can examine how deep learning helps keep birdseed for the birds. In the following, we will explore how to design an image classification solution using the Deep Learning Workbench (DL Workbench).