Clear and concise. I've always appreciated the similarity between SageMaker and Jupyter notebooks in their UI distribution. Perhaps I'm a nostalgic Data Scientist who prefers a simple canvas to work on. Hehe.
@vidhyalakshmiparthasarathy85732 жыл бұрын
Hi @Julien Simon - Thank you sir for your great videos. I've learnt AWS SageMaker by following your youtube channel. Your explanation and contents are amazing. 👍 😀
@juliensimonfr2 жыл бұрын
Great to hear!
@caiyu538 Жыл бұрын
Great lectures.
@imnischaygowda4 жыл бұрын
In detail. Helpful. Thank you
@juliensimonfr4 жыл бұрын
You're welcome!
@omarpasha29683 жыл бұрын
I learned a lot!
@juliensimonfr3 жыл бұрын
great, thanks for watching.
@denisbrinzarei64114 жыл бұрын
Julien, could you please share the GIT repo for the project?
@juliensimonfr3 жыл бұрын
This should be the one: gitlab.com/juliensimon/awsdevdays2020/-/blob/master/mls1/XGBoost.ipynb
@NV3009 Жыл бұрын
Julien, Good one really, I have a quick question. Is Appstream 2.0 can be deployed via SCCM? Can i have the steps if this can be done?
@juliensimonfr Жыл бұрын
I know nothing about Appstream and SCCM.
@soumyajitganguly25933 жыл бұрын
how can we invoke the endpoint from outside the sagemaker studio session? any other video where this is explained?
@juliensimonfr3 жыл бұрын
Sure :) The endpoint is a regular HTTPS endpoint. You can HTTP POST to it, or use a Predictor in the SageMaker SDK docs.aws.amazon.com/sagemaker/latest/dg/neo-requests-sdk.html
@MirrorNeuron2 жыл бұрын
Hi @Julien, thanks for the video. I am looking for a tutorial where I can call a Sagemaker endpoint with a restAPI and continuously run inference. Where can I find that? Please guide.
@juliensimonfr2 жыл бұрын
This should help aws.amazon.com/blogs/machine-learning/creating-a-machine-learning-powered-rest-api-with-amazon-api-gateway-mapping-templates-and-amazon-sagemaker/