Project Brainwave provides hardware accelerated machine learning with FPGA.
In Github tutorial, there are several useful helper classes and functions (with python) which encapsulate boilerplate code to achieve provisioning steps. In this post I show you the same steps without these helpers. With these steps I hope it helps you to understand new FPGA-enabled services and how it’s working.
Here I show you TensorFlowOnSpark on Azure Databricks. With this tutorial, you can learn how to use Azure Databricks through lifecycle, such as – cluster management, analytics by notebook, working with external libraries, working with surrounding Azure services, submitting a job for production, etc.
In this post, we quick view how to run the workloads with neural networks (deep learning workloads) in SQL Server.
In this post I show you several approaches for using SQL Server in-database machine learning workloads (R / Python workloads) with pros and cons.
By using aztk, you can easily deploy and drop your Spark cluster in the cloud (Azure) and you can take agility for parallel programming (for ex, starting with low-capacity VMs, performance testing with large size or GPU accelerated, etc) with massive cloud computing power.
Here I show you our machine learning tutorials (PySpark and MLlib) with aztk.
In this post I show you the tips for using admin consent for the scopes of Outlook REST API, 3rd party apps, or your own custom apps in Azure AD v2 endpoint. (Using UI, you can set the scopes only for Microsoft Graph.)
In this post I show you Azure Batch AI fundamentals (how to use and how it works) using Azure CLI. You can find that Batch AI significantly simplifies your distributed training with Azure infrastructure.