Azure AI Studio vs. IBM SPSS Modeler

I am deploying a lot of pipelines and making a lot of variants of these pipeline segments, like different types of vector search techniques. The simple way to mix and fix these segments to run the whole pipelines in notebooks options are big overhead killer. The playground which provides a test sandbox helps a lot to evaluate LMs if they are the best fit for our use-case even before deploying and startup with the costing angle.

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Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

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• Wide Catalog of Models is a beautiful feature for all who want to evaluate a lot of models before proceeding with any client use case for the best performance.
• Playground for testing and evaluating visual comparisons on so many metrics like latency, cost, and output time.
• Integration with other Cloud services, this makes the full complete solutioning and vision complete, from storage to compute.

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• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

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• Model Catalog can have a feature to show basic compute and the cost of running the model on that compute. With latency metrics, I generally need to do a lot of research before losing some dollars on deployment on hit and trials.
• Documentation generator for pipelines deployed in notebooks, generally developers use notebooks for experimentation, where logging them can be a big overhead.

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• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

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Microsoft Foundry includes the existing AI Services. It's quite literally Cognitive Services under the hood, so in that sense, if you're building a new "AI app" today where you would have deployed an AI Services Account, you can deploy Microsoft Foundry instead. The side benefit of that is that Foundry also includes model deployments, and more than just the OpenAI models. So, it removes the need for deploying a separate Azure OpenAI Service in some circumstances. Then finally it has agent capabilities too. So, if you're developing and deploying agents as part of your solution (which usually interface with a model) then you can do that from there as well. Effectively it's meant to be a one-stop-shop for all things AI, just like Fabric is for data.

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The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

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The online support board is helpful and the free add ons are incredibly appreciated.

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Azure AI Studio were the pioneers of DevOPs, so MLOPs feels quite a bit better on this platform than Google. Azure brought OpenAI into the system which made the Organization to shift from any other platform to Azure AI Studio.

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When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

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• Onboarding a team member for the codebase is slightly slower, almost 20% slower. As codebase sharing is like a git pull from repos, whereas here we need to provide all the access.
• I have experienced scaling up speed almost 50% faster as per compared with on-prem solutions. ML models are faster deployed in terms of on-prem deployments.
• 10 times better Azure AI Studio for cost visibility over any other solution.

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• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

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Independently Invited by TrustRadius

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