Azure Machine Learning vs. IBM Watson Natural Language Understanding

I would highly recommend Azure machine learning design for those with less access to high-end computing infrastructure, as using Azure saves a lot of time, money, and effort by providing a hustle-free platform that is easy to use and train your employees on. On the other hand, if you are looking for complete control of the machine learning model you create and would like to add detailed functionalities and try different algorithms, then Azure is less suitable here as it’s very high level.

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IBM Watson Natural Language Understanding is a Swiss Army knife that can be used in many scenarios. An extensive list of easy to use APIs is provided making it very easy to integrate it in any environment. The text analysis is decent and above market average. It generates results in many forms to suit may scenarios (important keywords, concepts, sentiment analysis, etc.).

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• 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.

• Easy to use and extensive APIs.
• Decent accuracy.
• It recognizes concepts and semantic roles.

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• 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.

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• Improve Sentiment Analysis accuracy.
• Prevent having conflicting results (sad and happy, etc.).
• Foreign names detection.

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Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.

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I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.

Not sure

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.

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• 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).

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• Reduced development time.
• Increased solution efficiency in understanding the user.
• Increased solution scalability.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info