Amazon Comprehend vs. Dataiku

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.

Incentivized

Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.

Incentivized

• 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.

Incentivized

• Allows users to collaborate and monitor individual tasks
• Caters to both types of analysts, coders and non-coders, alike
• Integrate graphs and plots with visualization tools such as Tableau

Incentivized

• 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.

Incentivized

• The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
• When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
• Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.

Incentivized

The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

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.

Incentivized

Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.

Incentivized

• 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.

Incentivized

• Customer satisfaction
• Timely project delivery

Incentivized