Likelihood to Recommend Specifically, it starts processing millions of documents in minutes by leveraging the power of machine learning without having trained models from scratch. If any of the content contains personally identifiable information not only can Amazon Comprehend locate it but it will also redact or mask it. Using NLP techniques Amazon Comprehend goes well beyond keyword search or rules-based tagging to accurately classify documents. For my task or development, I cannot find any difficulties with Amazon Comprehend.
Read full review 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 Pros Amazon Comprehend identifies the language of the text and extracts Key-phrases, places, people, brands or events. It can build a custom set of entities or text classification models that are tailored uniquely to the organisation's need Amazon Comprehend's medical can be used to identify medical conditions, medications, dosages, strength and frequencies from sources like doctor's notes, clinical trial reports and patient health records. This service is very good and with well an accuracy or confidence score. Read full review 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 Cons It will be great if Amazon Comprehend provide support specifically for litigation or related text documents to extract insights from it. For REST API support using JAVA SDK, it will be great for developers if they provide support for testing without any credentials or account details. Setting up for REST API integration can be as simple as possible. Read full review 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 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 For natural language processing tasks or techniques, there are many service providers out there in the market such as Azure Cloud Services, IBM Watson and Google Cloud Platform (GCP), but compared with them, Amazon Comprehend is the best service provider in contents of accuracy, speed of processing multilingual text, supporting SDK for most of the languages and well documented.
Read full review 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 Return on Investment It supports better and accurately as compared with our existing or old implementations. So, we fulfil our needs as per clients' requirements and it will help to grow or improve client satisfaction. For these specific requirements, we do not require any machine learning engineers or related professionals to hire in our organisation. None of any negative sides can be affected our business or distract existing clients. Read full review 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 ScreenShots