Name: Azure Machine Learning
Rating: 8.2 (33 reviews)
Author: Microsoft

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Azure Machine Learning

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

Machine Learning

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Posit

MBA Candidate | Class of 2018

Microsoft Azure Machine Learning Workbench 2018-05-16 20:36:20

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Azure Machine Learning Studio 2020-10-22 13:09:37

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Microsoft Azure Machine Learning Workbench 2019-05-07 12:09:43

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Machine Learning Tools

I was the president of an MBA class that used Azure ML to run analytics models. The tool was used by 40 students. We analyzed a few datasets to understand the tools, and afterward, we were able to create a few analytics products based on Azure ML.

User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
Tableau interface: last time I checked there was no easy way to connect with Tableau.
Cloud based: You always need a good internet connection to use it.

Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

Tableau Desktop, RStudio, KNIME Analytics Platform and Adobe Analytics

Microsoft Power BI, RStudio, Adobe Analytics, KNIME Analytics Platform, JMP Statistical Discovery Software from SAS, Oracle Business Intelligence Cloud Service

In the AI era, we need to build and deploy the machine learning model. Currently in our project is using the Azure Machine learning studio to preprocessing, cleaning, training and deployment of ML model as client requirement. As my knowledge in my team are using the Azure ML Studio. Currently, we are working to build the semantic text analysis of the documents.

Easy to create the experiment.
Easy to adopt the best algorithm.
Efficient way to deploy the model as a web service.
Centralized platform for the life cycle of machine learning goal.

Difficult to integrate the data for creating the model.
I feels it's costly to use it.

It reduce[d] the time and cost of the development, testing and deployment of the ML model.
Easy to track the model.
Nowaday[s] we are addicted with the cloud services.

Amazon SageMaker, Microsoft Power BI, IBM Watson Studio (formerly IBM Data Science Experience)

Text analysis.
Semantic text.
Search the semantic keywords.

Price
Product Features
Product Usability
Product Reputation
Analyst Reports

I create data science learning materials on Azure that require no coding. I use publicly available property data from Hong Kong island and surrounding areas. I teach my students how to preprocess the data, clean it up and create a hypothesis based on the type of data. We apply learning algorithms on the data and improve on the mode. The dataset was relatively small yet it took a while for the platform to get the analysis.

Adding python scripts
Pre-trained models
Case studies of industry projects

It would be great to have text tips that could ease new users to the platform, especially if an error shows up
Scenario-based documentation
Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

Currently, it is used for our information technology sector to implement machine learning features in-house. The idea is to explore models and perform some experimentation. It's used to find Machine Learning solutions for internal use in the company. The Microsoft resources in this tool make it easier to use machine learning, like the use of visual interfaces and how they manage deployment.

Visual interface
Possibility to track the IDs and also get the results from it
Charts to collect data and quickly check for performance/problems

Hard to apply Python code and run
More models could be available
Tableau interface would be perfect

It is easy to learn and construct, which impacts directly on productivity.
Good for experimentation and validation for simple models.
Has a use cost less than the best alternatives in the market.

Cloudera Data Science Workbench, Amazon SageMaker and Google Cloud AI

Azure Machine Learning

Overview

What is Azure Machine Learning?