Amazon Rekognition vs. Dataiku

Amazon Rekognition is well suited for all image and videos analysis. Also, deep learning projects for image and video can easily be done using this. It is very easy to use so a beginner can also use it.

Incentivized

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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• Image tagging is very good.
• Object detection is precise.
• Video tagging has become very easy using Rekognition.

Incentivized

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

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• The cost is bit more for small scale companies.
• For text processing, OCR is not available in amazon rekognition.
• Only facial search is available in image search.

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• End product deployment.

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Very much suitable for many applications where the image processing features are secondary and independent of any domain. This makes it a general solution and the recognition features are returned in a JSON object in response to the API called made which is a very simple process.

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As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

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Support of Amazon is great. So far we are having a great experience using it.

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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It's easy to implement and its documentation is great compared to CNTK.

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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• The best thing it made our work fast.
• It reduces the chance of failure of a project which is based on image or video processing.
• We can assign anyone in our team to work on this as it very easy to use.

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• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized