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Deployment of Machine Learning Models

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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