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Azure Machine Learning

Score8.2 out of 10

34 Reviews and Ratings

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

Azure Machine Learning Reviews

What users are saying about Azure Machine Learning.
View all Azure Machine Learning reviews

Making AI products never been easier.

Incentivized
Verified User
Professional in Information Technology (1001-5000 employees)

Use Cases and Deployment Scope

I use Azure to build, train, and deploy machine learning models using Python SDK. It’s great in way that it allows several ways of software design. For businesses and institutions, Azure saves the cost and time needed to start building and running big machine learning models and deep learning neural networks, this is done through the cloud so we do have to buy expensive equipment and GPUs or even go into the hustle of understanding which one to buy. It was useful during COVID as it allows remote working using the cloud-based services, which can run on any machine with good access to the internet; I was able to run big machine learning models using just my notebook. The other good thing about it is that it’s energy efficient; with all the huge computing running on the cloud, we are not really consuming any energy, and I can leave it running on the cloud and turn off my laptop; this is great when compared to my other local machines with GPUs where I need to keep it running day and night until the models finish running. In addition to consuming a lot of energy, it also means I’ll not be able to use my machine to run any other processes. Luckily with Azure Machine learning designer, this is something from the past.

Pros

  • Leverage machine learning as a service.
  • Accelerate machine learning with best-breed algorithms.
  • Support remote working with cloud-based services.
  • Creation of compliant and secure machine learning applications.

Cons

  • Limited storage for those using the free version.
  • Limited number of machine learning algorithms.
  • Because of its high-level design, it doesn’t give the developers much control as you don’t write much code in to do what you exactly want.

Most Important Features

  • Help students build AI and machine learning models easily and quickly, making it a good in-class digital resource.
  • Saves money, time, and effort by providing you access to high-end computational power.
  • Facilitates remote and team working with its cloud-based services.

Return on Investment

  • Reduce energy consumption caused by GPUs.
  • Saves on recycling and transporting costs and maintenance caused by buying high-end equipment.
  • Improve productivity as building products using Azure is easier than building everything up from scratch (e.g., machine learning and AI applications).

Alternatives Considered

Amazon Deep Learning AMIs

Other Software Used

Amazon Deep Learning AMIs, Google Cloud AI, OpenAI

Azure ML: Most user friendly and the cheapest!

Incentivized
Gabriel ChiarariaView profile
MBA Candidate | Class of 2018 in Marketing at Brigham Young University Marriott School of Business (201-500 employees)

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

Verified User
Professional in Information Technology (10,001+ employees)

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

Incentivized
Verified User
Professional in Information Technology (1001-5000 employees)

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

Incentivized
Verified User
Team Lead in Information Technology (10,001+ employees)

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