Azure AI Studio vs. Jupyter Notebook

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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I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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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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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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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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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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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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Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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I haven't had a need to contact support. However, all required help is out there in public forums.

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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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With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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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 impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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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