In this post I show you top 7 key benefits for Azure Machine Learning service using programming code along with the development lifecycle.
With Azure Dev Spaces, you can build and debug microservices in Kubernetes cluster without installing entire system in your local desktop.
In this post I introduce Azure Dev Spaces using command line, and see how it works.
In this post I introduce NVIDIA TensorRT for Azure engineers (data scientists).
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 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.