Azure AI Studio vs. Explorium

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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We need to constantly measures costs in our health business and we forecast pricing acoording to several values and conditions. Explorium works quite good analysing simple datasets, but when hierahies start to increase, meaning 6-10 olap variables, the system start to slow down quite a bit until was no longer to retrieve the info we required. This is why we test several tools, because even world-class solutions we purchase, don´t do the job we need. Explorium is a good tool, but complexity will be a minus in some scenarios.

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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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• Data ready for consumption.
• Good enough relationships between entities.
• Nice integration with other players. That's a good thing.

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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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• relationships between entities could be better. Would be great to have scenarios.
• The data normalized needs improvement. Works pretty good, but it needs more refinement when using AND-OR Formulas that came from various datasets.

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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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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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The simplicity of the tool is an advantage. The integrations as well work quite well. All these solutions have worked well until some point and what we have discovered over the years is that we need to combine various solutions. There is no such thing as one tool ruling them all. Explorium works quite well until we start testing more advanced relations, and here, the tool is promising but requires a little work.

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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. The time save in analysis per hour of internal consulting was the best compared to our SAP and Cognos solutions.
• In term of objetives works as stated, but when using multi-conditions of dataset, start the issues.

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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