Amazon Comprehend vs. Azure Databricks

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

Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.

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

• Data Processing and Transformations based on Spark
• Delta Lakehouse when clubbed with an external cloud storage
• Governance using Unity Catalog to unify IAM
• Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.

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

• Intuitive interface
• Ease of use
• Providing FAQ or QRGs

Incentivized

Great for what we use day to day and does what we need it to do. Cost management is not fully developed across the UX and gets expensive very quickly for developing projects. Integrated very well with our Microsoft stack and can be worked on collaboratively which works well for us.

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

Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse

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

• Helped reduce time for collecting data
• Reduced cost in maintaining multiple data sources
• Access for multiple users and management of users/data in a single platform

Incentivized