Bridging the Gap Between AI Research and Production with Semantic Kernel

AI experts and enterprise development teams often use different tech stacks with different concepts and resource types. Now that Semantic Kernel is V1.0 in Python, C#, and Java though, your teams now have the opportunity to speak the same language. With Semantic Kernel, ML teams can create the same assets for plugins, planners, and agents, evaluate them, and then give them to app dev teams to deploy them.
Watch my colleague Matthew share how Semantic Kernel in all its flavors (Python, C#, and Java) can help you bridge the gap for building AI apps for your enterprise.
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[@ArafatTehsin]

Just came across this, right after my break. Thanks for sharing it :)

[@greenyngchannel855]

Excellent!!! Let's see one day you inspire us to use Semantic Kernel within the world of IoT device, mcu, with nanoFramework... I'm sure there would be thousands of interesting scenarios, we want to open that door

[@tomthunderforest1681]

Semantic Kernel rules! Thank you guys!

[@darenbaker4569]

Brilliant and so right sk is so good

[@brettgarnier107]

Hi, I'm trying to get caught up but it seems that the Github repo for sk's notebooks now reference missing folders. It seems like the blog/github/notebooks/videos are not all aligned and I'm not sure what is the source of truth for learning. What resource is the most current?

[@florimmaxhuni4718]

HI
Where can we find all this sample code ?

[@anatolydyatlov963]

08:24 seriously, how is that "huge"? To me, it seems like an over-glorified wrapper for function calling. Creating a simple flow where the LLM can call multiple functions in a single request is more trivial than installing and using this SDK

[@eric4143]

don't you have a reactor to repair?

[@anatolydyatlov963]

@@eric4143 I was replaced with GPT-4