Examples of deploying scikit, spaCy and Keras (TensorFlow) machine learning models to AWS Lambda with Serverless framework and Python 3.
Examples of deploying machine learning models to AWS Lambda with Serverless framework and Python 3. This repository is a code base for the online course available on the address above.
Hello World example of creating a project with Serverless and deploying to AWS Lambda in Python.
An example of Serverless project which contains machine learning regression model from scikit-learn trained on California housing dataset.
An example of Serverless project which uses a small English model from spaCy NLP framework to create an AWS Lambda endpoint for named entity recognition. NOTE: .requirements.zip file created with serverless-python-requirements is omitted due to its size.
An example of Serverless project which uses a small English model from spaCy NLP framework to create an AWS Lambda endpoint for parts of speech tagging and dependency parsing. NOTE: .requirements.zip file created with serverless-python-requirements is omitted due to its size.
An example of Serverless project which uses ResNet50 computer vision deep learning model from Keras framework to create an AWS Lambda endpoint for image recognition. The example requires two S3 buckets (one for model storage and other for image uploads) and optional AWS Identity Pool configuration. In tensorflow and PIL folders .so files are omitted due to their size. You should replace those folders with complete tensorflow and PIL installations.
An example very similar to above keras-example, but uses InceptionV3 model instead of ResNet50.
Contains a web page for sending requests to endpoints created with above examples. You have to create your own endpoints and add their URLs and other credentials to js/app.js file.