Deploying Model on AWS (3 of 3 Live Coding Webinar Series)

Description:

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. Sagemaker provides many built-in algorithms, support for algorithms written in frameworks like PyTorch and Tensorflow as well as custom algorithms from scratch too. AWS suite of tools also includes Lambda which is a serverless compute platform, S3 for storage, API Gateway for sending/receiving API requests from a client or server and many others.

We will be using Sagemaker to deploy our PyTorch BERT Model, S3 for storage purposes, and API Gateway and Lambda to bridge the communication between our React frontend and the deployed model. This is the last part of the 3-part Full-Stack AI Deployment series. This is also a live coding session, so you can code along to learn best.

Prerequisites:

* Should have attended the last two parts of this series
* Compulsory AWS Account for a hands-on experience (Free-tier account will work, if you have trouble setting up the AWS account let us know)
* Python
This hands-on webinar will be useful only if you have met the prerequisites mentioned above.

Presenter:

Bhavesh Laddagiri
1st. Yr. B.Tech. (SRM University)
AI Researcher, CellStrat AILab