Tag Archives: Azure

Microsoft Weekly Data Science News for September 07, 2018

Here are the latest articles from Microsoft regarding cloud data science products and updates. This week it includes Measuring Model Goodness, a free ebook, AI discovery days, and more AI goodness.

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Microsoft Weekly Data Science News for August 31, 2018

Here are the latest articles from Microsoft regarding cloud data science products and updates. Find the latest on AI, Azure Functions, IoT and more.

Microsoft Weekly Data Science News for August 24, 2018

The latest articles from Microsoft regarding cloud data science products and updates.

  • Build Intelligent Web App with Machine Learning ServiceIn an earlier article: How to operationalize TensorFlow models in Microsoft Machine Learning Server, we showed how you can deploy a TensorFlow image classification model pre-trained using ImageNet as service in Machine Learning Server, and download a …[Read More]
  • Measuring Model Goodness – Part 1Data and AI are transforming businesses worldwide from finance … actionable insights. This is where data science and machine learning come in. This entire process has been documented as the Team Data Science Process (TDSP) at Microsoft, captured in …[Read More]
  • Driving industry transformation with Azure – Getting started – Edition 1Banking customers want more from their services … using AI and machine learning to detect patient risk and identify disease faster while maintaining privacy and protecting against fraud. IoT in manufacturing isn’t just about collecting data.[Read More]
  • Speech Services August 2018 updateWe are pleased to announce the release of another update to the Cognitive Services Speech SDK (version 0.6.0 … dev kits to significantly improve the audio quality of the audio data collected via the dev kits’ microphones, for high speech recognition …[Read More]
  • Introducing yet another approach for iot compiler toolchains – iotziotz is an extension based containerized wrapper for other IoT compiler toolchains. There are many toolchains with specific needs and way of using. We developed this experimental tool to make compiling things easier. – cross compiling tools are mostly …[Read More]
  • Design against crime & Microsoft Azure with ShinyIt is this app we will later learn how to deploy a shiny app onto Microsoft Azure services … including a review of data available via police.uk. We engaged with UCL to find a student from the Security and Crime Science course who could help us to …[Read More]
  • Simplifying big data analytics architecturemanufacturing, retail education, nonprofit, government, healthcare, media, banking, telecommunication, insurance, and many more industries ranging in use cases from ETL to Data Warehousing, from Machine Learning to IoT, and more.[Read More]

Microsoft Weekly Data Science News for August 17, 2018

Here are the latest articles from Microsoft regarding cloud data science products and updates.

Azure Functions for Data Science

Data Scientists do more than build fancy AI and machine learning models. They often times need to get involved with the data acquisition process. It is common for data to be pulled from other databases or even an API. Plus, the models need to be deployed. These tasks fall to the data scientist to solve (unless there is a data engineer willing to help). Recently, I have discovered Azure Functions to be an extremely useful tool for solving these types of tasks.

What are Azure Functions?

Simply stated, Azure Functions are pieces of code that run. More formally stated,

Azure Functions are serverless computing which allows code to run on-demand without the need to manage servers or hardware infrastructure.

This is exactly what a data scientist needs to solve the tasks mentioned above. I, for one, do not enjoy managing servers (hardware or virtual). I have done it before, but I find it time-consuming and tedious. It is just not my thing. Thus, I happily welcome the serverless capabilities of Azure Functions. I just focus on the code and get the task completed.

Because the code does not always need to be running, Azure Functions invoke the code based upon specified triggers. Once the trigger is activated, the code will begin to run. The following list provides some examples of the triggers available.

Triggers for Azure Functions:
  1. Timer – Set a timer to run the Azure Function as often as you like. Timing is specified with a cron expression.
  2. HTTP Rest call – Have some other code fire off an HTTP request to start the Azure Function.
  3. Blob storage – Run the Azure Function whenever a new file is added to a Blob storage account.
  4. Event Hubs – Event Hubs are often used for collecting real-time data, and this integration offers Azure Functions the ability to run when a real-time event occurs.
  5. Others – Cosmos DB, Service Bus, IoT Hub, GitHub are other events which can trigger an Azure Function.

What can Azure Functions Do?

Once you begin to understand the concept, you can quickly see some of the possibilities. Without having to configure servers or virtual machines, the following tasks become much simpler:

  • Reading and writing data from a database
  • Processing images
  • Interacting with an HTTP endpoint
  • Automating decisions in real-time
  • Computing descriptive statistics
  • Creating your own endpoint for other data scientists to call
  • Automatically analyzing code after commits
Programming Languages for Azure Functions

As of August 2018, full support is provided for C#, Javascript, and F#. Experimental support is provided for Batch, PowerShell, Python, and TypeScript. Python can be used to create an HTTP endpoint. This would allow someone to quickly create an endpoint for running machine learning models via scikit-learn or another python module. Unfortunately, R is not yet available, but Microsoft has a lot invested in R, so I am expecting this eventually.

Simplify Tasks for Data Science

Next time you have a data science task which requires a little coding, consider using an Azure Function to run the code. It will most likely save you some deployment and configuration time. Then you can quickly get back to optimizing those fancy AI models.

See the video below for a quick demonstration of how to create an Azure function via a web browser (no IDE needed).