Azure OpenAI Service vs. IBM Watson Studio on Cloud Pak for Data

If you're looking for a managed OpenAI API service, then Azure OpenAI Service is a good choice.

It's fully compatible with OpenAI API, has lots of models to choose from, lots of parameters to configure to suite your needs.

The documents are well maintained, with examples to get started.

You can also setup firewall to restrict access to the API to certain IP addresses, like those of your VPCs.

Incentivized

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.

Incentivized

• Generating SQL
• Generating Images
• Sensible Response

Incentivized

• 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.

Incentivized

• The user interface is similar to the Azure, so it's bit laggy, they should improve it.
• The chat models sometimes return nothing, I guess they are still testing them.
• The models we use have quota limitation on tokens and number of requests.

Incentivized

• 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

Incentivized

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

Incentivized

I think it's a good product and appreciate the addition secure guard rails that running it in Azure provide. However, I still struggle at times to get to the right resources for support and region-based capacity can also be a challenge.

Incentivized

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

Incentivized

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

Incentivized

Never had slow response even on our very busy network

Incentivized

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

Incentivized

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

Incentivized

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

Incentivized

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

Incentivized

1. Open AI is best at giving accurate answers. 2. It is secure and more trustworthy 3. Most of our client using Azure cloud so it becomes go to choice for them. 4. Scalable as it handles 1000s of request per minute. 5. SDKs are easy to use and well documented.

Incentivized

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.

Incentivized

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

Incentivized

• Our test group of 100 people created more than 200 conversations within the first day
• People were excited & asked to be included to the test group once words spread that we have an internal ChatGPT portal
• People started to create & share their own assistants (prompt engineering) for various purposes like code review, marketing, email, etc.

Incentivized

• 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