IBM InfoSphere Information Server vs. IBM Watson Visual Recognition (discontinued)

Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

As I mentioned before, it can only be employed in simple basic visual recognition applications. It can be employed in large projects and relying it on completely is not encouraged. It's better to create your own algorithms rather than using it. If you are from a non-programming background, then I may suggest you rely on this and use it to develop simple apps that can predict a few plants and animals.

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• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

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• Food recognition
• High reliability
• Speed

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• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

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• Not perfect
• Accuracy is doubted and sometimes it may not predict correctly.
• I think it should be improved and should add a few more functionalities.

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• Scale of implementation
• IBM techsupport

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I am giving this rating on the basis of its usability in real-time applications and based on the interface to upload negative and positive images to train the AI. But it's not perfect and sometimes its predictions are wrong. On overall usability, it's better if you are planning on working with UI rather than using complex programs and algorithms on your own.

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DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• High cost if models are trained

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