Deploying Hugging Face Models in Sagemaker:Introducing AWS Sagemaker to Create Inference End points

The video discusses the way of loading the Hugging Face AI models into AWS Sagemaker, and creating inference endpoints. It starts by introducing the Sagemaker product inside AWS, and then the challenges it solves. We discuss the Hugging Face model loading into S3 bucket for faster access and inference end point creation. The data and the code is located at https://github.com/insightbuilder/python_de_learners_data/blob/main/code_script_notebooks/projects/huggingface_AWS/storing_model_s3.ipynb The 8 steps required to initiate the Sagemaker instance, pulling the model into the Sagemaker execution environment and finally deploy the endpoint is discussed in this video. A small tour of sagemaker page inside the AWS is given. In the next video, the creation of the instance and deployment of the Hugging face models as inference endpoints. Hope you like this video, and subscribe to the channel. Further uploads related to Big Data, Large Language models and Artificial Intelligence will be shared to your Youtube Dashboard Directly. The supporting playlists are Python Data Engineering Playlist https://www.youtube.com/playlist?list=PLbzjzOKeYPCo_hMXIl2URu7GL33-4_Yy0 Python Ecosystem of Libraries https://www.youtube.com/playlist?list=PLbzjzOKeYPCoNAsZs679iXwsdP44G5SDS ChatGPT and AI Playlist https://www.youtube.com/playlist?list=PLbzjzOKeYPCpp3NCeQioevM0YpZa5VqcS AWS and Python AWS Wrangler https://www.youtube.com/playlist?list=PLbzjzOKeYPCogrhYDBgRNJDPV2CCwGrFT PS: Got a question or have a feedback on my content. Get in touch By leaving a Comment in the video @mail [email protected] @twitter Handle is @KQrios @medium https://medium.com/@kamaljp/about @github https://github.com/Kamalabot