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.
The advantage of CNTK (Cognitive Toolkit) is not only performance, but it can also support multiple GPUs on multiple machines with built-in capabilities and rapidly scale along with the number of nodes and GPUs. Here I show you the step-by-step walkthrough for the distributed training with CNTK.
In this post I show you Azure Batch fundamentals (how to use and how it works) using Azure CLI. Azure Batch is so useful for your batch execution in cloud and essential understanding helps you to use it for various scenarios like AI (training) or many kinds of job execution.