Likelihood to Recommend For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.
All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.
If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.
Read full review It's easy for anyone who is expecting some simple AI problems like fetching the keywords, understanding the intent, language translation, etc. to be solved from an existing database and all they need is to connect to their APIs via a subscription model. But for complex use cases, there is still room for improvement like customization of underlying AI models for a specific use case like identifying some unique identifiers with respect to industry.
Read full review 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. Read full review Being hosted in Azure solves a massive hosting problem The language understanding system has the ability to revolutionize many vertical markets Integrating with Cortana Analytics was really simple due to easy to understand documentation Read full review 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 Read full review More partnerships with colleges and schools to increase the workforce with technical knowledge (increase local workforce) Have more online training and documentation in other languages Have affordable prices for students Read full review Usability Easy and fastest way to develop, test, deploy and monitor the machine learning model.
- Easy to load the data set
-Drag and drop the process of the Machine learning life cycle.
Read full review Support Rating Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.
Read full review Implementation Rating Not sure
Read full review Alternatives Considered It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.
Read full review IBM Watson Assistant has been early into this market and has improved a lot over time compared to Azure AI Cortana. More documentation related to the services. But Ease of integration Azure AI ranks over
IBM Watson Assistant . And again in terms of services offered under the ecosystem, Azure AI precedes IBM Watson Assitant.
Read full review 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 Read full review Difficult to ascertain the ROI as we are a software house who have developed a module in our application using Cortana. However for companies that use our software I would say the use of sentiment analysis in our application could free up at least 1 full time resource to be used elsewhere in their organisation. Read full review ScreenShots