Deployment of Machine Learning Models

• Build machine learning model APIs and deploy models into the cloud
• Send and receive requests from deployed machine learning models
• Design testable, version controlled and reproducible production code for model deployment
• Create continuous and automated integrations to deploy your models
• Understand the optimal machine learning architecture
• Understand the different resources available to productionise your models
• Identify and mitigate the challenges of putting models in production

Specifically, you will learn:

• The steps involved in a typical machine learning pipeline
• How a data scientist works in the research environment
• How to transform the code in Jupyter notebooks into production code
• How to write production code, including introduction to tests, logging and OOP
• How to deploy the model and serve predictions from an API
• How to create a Python Package
• How to deploy into a realistic production environment
• How to use docker to control software and model versions
• How to add a CI/CD layer
• How to determine that the deployed model reproduces the one created in the research environment

Who this course is for:

• Data scientists who want to deploy their first machine learning model
• Data scientists who want to learn best practices model deployment
• Software developers who want to transition into machine learning