A pre-trained ML(machine learning) model is an entity that accepts an input payload and returns you a prediction. It’s a mathematical model that generates predictions by finding patterns in your data.
A pre-trained ML model typically solves a type of a problem. * For example, there is an ML model that accepts a car’s picture as an input and performs a prediction (a.k.a inference) returning the make, model, and year of the car. * There is another pre-trained machine learning model that identifies whether a person is wearing a mask or not.
Typically, machine learning models can accept text, image, audio, video, tabular data and generate prediction in different forms such as a number (e.g. price of a house), a class(e.g. positive vs negative sentiment), bounding boxes of objects identified in image/video data, etc.
Using a pre-trained model means you can get around the heavy lifting of hiring ML resources and training/tuning ML models from scratch.
ML Models are deployed to perform two types of inferences (or predictions):
Pre-trained Machine Learning (ML) models from AWS Marketplace are ready-to-use models that can be quickly deployed on Amazon SageMaker, a fully managed cloud machine learning service. You can purchase paid pre-trained ML models or use free pre-trained ML models, via AWS Marketplace.
Watch first 7 minutes of the following video.
For today’s lab, you will be using Resnet-18 ML model, a general purpose free ML model that accepts a picture and returns probabilities for 1000 different object categories. You will deploy the ML model for real-time inference and then you will perform the inference.
Step 2 - Try Resnet-18 ML Model product from AWS Marketplace
The ML model you are going to try today requires an image as the input payload. Download this sample image to your computer.
Congratulations, you just performed a prediction on an ML model. Note how an ML model was able to identify intelligent attributes from the image.
Step 3 - Subscribe Resnet-18 ML Model product from AWS Marketplace
Congratulations, you have just completed step 2 from the following architecture diagram.