Deployment of Machine Learning Models

Making your models accessible to other systems within your organization or on the web so they can receive data and provide predictions is known as "deploying machine learning models" or "putting models into production." You can start to fully utilize the model you created by deploying machine learning models.

The course will walk you through every step of creating a model in a research environment, turning Jupyter notebooks into production code, packaging the code and deploying to an API, and adding continuous integration and continuous delivery. The course will do this by walking you through it step-by-step through engaging video tutorials. You will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And you will also discuss the tools and platforms available to deploy machine learning models.

By the end of the course, you will have a thorough understanding of the machine learning model's research, development, and deployment lifecycles, as well as the best coding practices and factors to take into account when putting a model into use. Additionally, you'll be better equipped to use the tools at your disposal to deploy your models in any way that meets the needs of your company because you'll have a better understanding of them.

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

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

Course ratings: 4.5/5

Enroll here: https://www.udemy.com/course/deployment-of-machine-learning-models/