Amazon/Intel Confidentia Presenter: Zhi Chen, Cody Yu Amazon SageMaker Neo, Deep Engine Science Bring Your Own Codegen to TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Considering Prototyping https://github.com/apache/incubator-tvm/pull/4258 RFC https://discuss.tvm.ai/t/bring-your-own-codegen-to-tvm/4501© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Acknowledgement

from AI adoption are often the ones that invest time and resources in customizing and training their own AI models. OpenAI has invested heavily in our API to make it easier to customize and fine-tune models—whether ensure responsible use. They rolled out ChatGPT Enterprise globally, then let people discover their own use cases. “Normally, in a business like ours, building even a prototype requires technical resources and beyond. All because they got AI in the hands of the people who know how to apply it in their own disciplines. We consider our investment in ChatGPT an investment in our people. AI amplifies our

researchers deploy Shakey, the first general- purpose mobile robot that can reason about its own actions 5/97: Deep Blue, IBM’s chess- playing computer, defeats Garry Kasparov, the world 1,792 Square Feet We were told it would take 24 months to build. So we took the project into our own hands, questioned everything, removed whatever was unnecessary, and accomplished our goal in four Services The top cloud providers offer platforms that help businesses leverage AI tech in their own products and workflows. Consumer AI Monetization Possibilities = New Entrants & / Or Tech Incumbents

is also possible that the sender is a malicious actor who is trying to exploit the bug for their own gain. **Conclusion:** Based on the above factors, the email should be classified as **IMPORTANT**. showcase desired outputs or similar responses, allowing the model to learn from them and tailor its own generation accordingly. It’s like giving the model a reference point or target to aim for, improving reference that variable in each prompt. This makes a lot of sense when integrating prompts into your own applications. Prompt VARIABLES {city} = "Amsterdam" PROMPT You are a travel guide. Tell me a fact about

contains multiple stages, performance limited by slowest one ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github.com/Xilinx/ml-suite/blob/master/examples/dep