Vertex AI vs. IBM Watson Studio on Cloud Pak for Data

Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.

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It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

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• Vertex AI comes with support for LOTs of LLMs out of the box
• MLOps tools are available that help to standardize operational aspects
• Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers

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• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

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Independently Invited by TrustRadius

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• Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
• Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
• Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users

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• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

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Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

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Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

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It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.

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Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info

Never had slow response even on our very busy network

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

The trainers on the job are very smart with solutions and very able in teaching

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

The Platform is very handy and suggests further steps according my previous interests

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

It surprised us with unpredictable case of use and brand new points of view

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info

It helped us in getting from 0 to DSX without getting lost

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
• It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
• It'll help you in terms of Security wherein we can use row level security access to authorized persons.

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Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized

Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info