Deploy Serverless Machine Learning Models to AWS Lambda | Udemy
Pure serverless machine learning inference with AWS Lambda and Layers. After designing and learning a ML model, the hardest part is actually running and maintaining it in production. AWS is offering to host and deploy models via On-Demand ML Hosting on various instance types and sizes, packaged as the SageMaker service NEW
What you'll learn
- Deploy regression, NLP and computer vision machine learning models to scalable AWS Lambda environment
- How to effectively prepare scikit-learn, spaCy and Keras / Tensorflow frameworks for deployment
- How to use basics of AWS and Serverless Framework
- How to monitor usage and secure access to deployed ML models and their APIs
- Created AWS Account
- Basic familiarity with Python and Machine Learning
- Basic undestanding of Linux and Terminal
By following course lectures, you will learn about Amazon Web Services, especially Lambda, API Gateway, S3, CloudWatch and others. You will be introduced with various real-life use cases which deploy different kinds of machine learning models, such as NLP, deep learning computer vision or regression models. We will use different ML frameworks - scikit-learn, spaCy, Keras / Tensorflow - and show how to prepare them for AWS Lambda. You will also be introduced with easy-to-use and effective Serverless Framework which makes Lambda creation and deployment very easy.
Although this course doesn't focus much on techniques for training and fine-tuning machine learning models, there will be some examples of training the model in Jupyter Notebook and usage of pre-trained models.