Category: Machine Learning

Tomaz Kastrun continues an advent series on Azure ML. Day 4 covers data sources:

Yesterday, we have learned the general outlook of the Studio and in this blog post, we will be focusing primarily on getting data to the workspace and reading data from other data sources.

Day 5 has you provision some compute:

With a basic understanding of data assets, let’s create compute instances. Under “Manage” in the navigation bar, select “Compute” (denoted as 1), select “Compute instances” (d. 2) and click on “+ New”.

Tomaz Kastrun continues an advent of Azure ML. First up, Tomaz creates a workspace:

You will select “New workspace”. For now, we will work on a workspace. But just to mention, the “New registry” will enable you to share assets among different workspaces, support multi-region replication and help you provision all resources to facilitate region replications.

From there, the focus shifts to using Azure Machine Learning Studio:

In this overview page, you can click the button “Launch studio” in the middle of the workspace or you can copy and paste the Studio web URL provided under the “Essentials” to start the Studio.

But before we launch the Studio, let’s explore some additional settings, worth mentioning.

Tomaz Kastrun has a new Advent challenge:

Azure Machine Learning (or Azure Machine Learning Service and abbreviation AML) is Azure’s cloud service for creating, managing and productionalising machine learning projects. It is a collaborative tool for Data Scientists, Machine Learning Engineers, and data engineers, covering their daily and operational tasks. From creating and training to deploying and managing predictive models and machine learning solutions.

Click through for the introduction.

Matt Eland creates a mega-bot:

The Azure Bot Service is effectively a registration for a conversational AI application on Azure. This registration allows you to connect a deployed chatbots to a wide number of supported channels that users can use to interact with the bot.

This lets you build one bot that can serve a variety of users across multiple different channels, including both text and voice channels.

Additionally, the Azure Bot Service gives you a centralized place to manage, secure, and monitor your bot, regardless of which channel people use to interact with your app.

Read on for an important caveat, as well as more information on Azure Bot Service.

Vishaal Kapoor, et al, take us through an example of pattern learning in Amazon SageMaker:

Pattern learning automatically analyzes your data and surfaces textual constraints that may apply to your dataset. For the example with phone numbers, pattern learning can analyze the data and identify that the vast majority of phone numbers follow the textual constraint [1-9][0-9]{2}-[0-9][4]. It can also alert you that there are examples of invalid data so that you can exclude or correct them.

In the following sections, we demonstrate how to use pattern learning in Data Wrangler using a fictional dataset of product categories and SKU (stock keeping unit) codes.

Read on for the scenario.