#1 most frequent
United States of America
49.1%5,100 installations of 10,393
What is Azure Machine Learning?
Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
Categories & Use Cases
Who Buys & Uses Azure Machine Learning
#2 most frequent
India
8.1%847 installations of 10,393
#3 most frequent
United Kingdom of Great Britain and Northern Ireland
6.3%653 installations of 10,393
Azure ML: Most user friendly and the cheapest!
Pros
- 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.
Cons
- 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.
Return on Investment
- 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
Alternatives Considered
Tableau Desktop, RStudio, KNIME Analytics Platform and Adobe Analytics
Other Software Used
Microsoft Power BI, RStudio, Adobe Analytics, KNIME Analytics Platform, JMP Statistical Discovery Software from SAS, Oracle Business Intelligence Cloud Service
Azure Machine Learning Studio Review
Pros
- 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.
Cons
- Difficult to integrate the data for creating the model.
- I feels it's costly to use it.
Return on Investment
- 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.
Usability
Other Software Used
Amazon SageMaker, Microsoft Power BI, IBM Watson Studio (formerly IBM Data Science Experience)
Azure Machine Learning studio is not ready for serious production use
Pros
- Adding python scripts
- Pre-trained models
- Case studies of industry projects
Cons
- 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
Usability
Machine learning tool that is easy to learn and use
Pros
- Visual interface
- Possibility to track the IDs and also get the results from it
- Charts to collect data and quickly check for performance/problems
Cons
- Hard to apply Python code and run
- More models could be available
- Tableau interface would be perfect
Return on Investment
- 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.
Alternatives Considered
Cloudera Data Science Workbench, Amazon SageMaker and Google Cloud AI
Other Software Used
Tableau Desktop, M‑Files, PowerApps