Deployment of Machine Learning Models

Deployment of Machine Learning Models - Learn how to integrate robust and reliable Machine Learning Pipelines in Production

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What you'll learn

• 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

Requirements

• A Python installation
• A Git installation
• Confidence in Python programming, including familiarity with Numpy, Pandas and Scikit-learn
• Familiarity with the use of IDEs, like Pycharm, Sublime, Spyder or similar
• Familiarity with writing Python scripts and running them from the command line interface
• Knowledge of basic git commands, including clone, fork, branch creation and branch checkout
• Knowledge of basic git commands, including git status, git add, git commit, git pull, git push
• Knowledge of basic CLI commands, including navigating folders and using Git and Python from the CLI
• Knowledge of Linear Regression and model evaluation metrics like the MSE and R2

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