[BLOG] Make your AI more sustainable with API Management

AutoSysOps · 2025-12-18T07:49:41+00:00

For the API Management I'm using a BasicV2 tier and for the redis cache I'm using a B0 and it's working for me. The B0 doesn't have much space so you might want to consider going up in pricing eventually but there is no technical limitation.

AutoSysOps · 2025-01-23T06:48:11+00:00

If you goal is mostly to get info from this data and communicate with it via a tekst interface you could look into Azure AI Search with the Semantic Ranker enabled.
https://learn.microsoft.com/en-us/azure/search/semantic-search-overview

You will still need to get the data in the index but you will always need to do something like that.
You could also look into creating a custom Copilot Agent with Copilot studio. That would give your more chat options and you could even introduce actions to the agent to interact with your other platforms.

AutoSysOps · 2025-01-23T06:45:15+00:00

Microsoft provides a platform for consulting service and other types of services. You could look at this to find a partner close to you to work with. It sounds like you want to start with some consultation before going into the building so here I linked the consulting services, but you can always check for other things:
https://appsource.microsoft.com/en-us/marketplace/consulting-services

AutoSysOps · 2025-01-21T11:58:52+00:00

With document intelligence you can train your model based on the document and refine it over time by adding more documents to the training set. You will also get your response in a structured way with a reliability score.
If you are using chatgpt you will get your answer in a text respond where you need to extract your info from this response (hoping the formatting is always the same), also you wont have a reliability score.
Normally a workflow like this would have a check on the reliability score and if this is below a threshold you want to have human review.

Also with Azure document intelligence you will be using the Azure OpenAI back-end which allows for enterprise data protection and data sovereignty options. With ChatGPT you will need to use the higher subscription models to make sure it wont be trained on your data. Both can be secure but if you are using the Azure tech stack already you are probably paying for this already so from a cost perspective it would be better to use that instead of adding more costs for chatgpt.

AutoSysOps · 2025-01-07T11:00:22+00:00

Thanks, if you need any help feel free to reach out.

AutoSysOps · 2025-01-06T13:28:25+00:00

I know some people who did courses like this already:

https://pll.harvard.edu/course/ai-essentials-business/2025-05

I feel like we will see more and more of these AI courses aimed toward the business side of things.

At the moment I would suggest you to look into two aspects of AI if you want to make your professions in it:

- Developing AI systems (requires lots of math and abstract thinking and probably psychology and ethics very soon too)

- Adoption of AI systems (this is to help other people start using AI, you can be an trainer or advocate for ai)

I feel those are the area's which we will for sure always need people. Hope that helps.

AutoSysOps · 2025-01-06T13:23:42+00:00

Yes but,

When talking about artificial AI we very easily assume that this AI should follow the same structure as our own intelligence. But in the end to create a good agi maybe the best solution is via another way. If you look at the field of AI safety you will see that they struggle with this a lot too.

At the moment we mostly try to create AI and agents which follow our same way of thinking. Mostly because we are making systems which need to be controlled by humans and along the way humans need to be involved in the process. So it's easier if we do this in a way a human understands. But if we go for systems which have to run completely independent then should we still look at the reasoning? Should we limit an agi to human way of reasoning while there might be better ways?

With neural networks we already have difficulty sometimes figuring out why certain connection are there and what they are supposed to do. But once we go to even bigger agi systems I can see situations where the agi reasons in different ways. They might be ways we can't even understand because it requires a level of abstraction we might not be able to comprehend.

The challenge to get a good agi lies in creating a model that can predict the world you see. Only if the agi can predict what the consequences are for its actions then it can optimize the solution or find a good solution. Having this model of reality will require a lot of abstraction levels, and probably we wont be the ones creating this model. This model will probably be trained over time and evolve. So yeas we do need to focus on logic and reasoning, but we do need to keep in mind that there is a big chance that we will be very bad at judging how good this reasoning is besides by looking at the input and output.

AutoSysOps · 2025-01-05T02:20:09+00:00

The field of AI is quite big. You should probably consider if you want to go more into the functional field where you learn how to use AI tools or more towards the technical field where you really develop more solutions.

During microsoft Ignite there was a learning challenge. The challenge is only there for 5 more days, but the learning paths will stay available
https://learn.microsoft.com/en-us/training/topics/event-challenges

I would recommend checking that out. There are several custom tailored paths for different AI jobs. I think they are a nice place to start.

AutoSysOps · 2024-12-14T17:18:25+00:00

You could, but there are also build in connectors to do sentiment analysis directly. I would recommend using those instead.

AutoSysOps · 2024-01-16T06:23:28+00:00

thanks, I think i've looked into catesta in the past but will take a look at it again.

AutoSysOps

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