As Machine Learning (ML) continues to grow in popularity and use in a variety of industries, there is an increasing need for ML models to be deployed for use in a production setting. The course Deployment of Machine Learning Models is designed to teach individuals how to do just that. With a comprehensive curriculum, this course covers everything from creating a model in the research environment, to transforming Jupyter notebooks into production code, and deploying models to an API using continuous integration and continuous delivery.
What is model deployment?
Before delving into the details of the course, it's important to understand what is meant by "model deployment." When a machine learning model has been built and trained in a research environment, the next step is to make it available for other systems and users to interact with and receive predictions. This is where the deployment of machine learning models comes into play.
Model deployment is the process of integrating the ML model into a production environment where it can be used to serve real-time predictions. In other words, it is the practice of making the ML model available to other systems within an organization or on the web.
What will you learn in the course?
The Deployment of Machine Learning Models course teaches individuals how to take their models from a research environment to a fully integrated production environment. The course includes over 100 lectures and 10 hours of video content, covering everything from the basic steps involved in a typical machine learning pipeline, to more advanced concepts like using docker to control software and model versions.
Individuals taking the course will learn how to:
- Create a model in the research environment
- Transform Jupyter notebooks into production code
- Write production code, including introduction to tests, logging and OOP
- Deploy the model and serve predictions from an API
- Create a Python package
- Deploy models to a realistic production environment
- Use docker to control software and model versions
- Add a CI/CD layer to their deployment process
- Determine that the deployed model reproduces the one created in the research environment
The course emphasizes the importance of reproducibility, and covers topics like versioning, code repositories, and the use of docker to ensure that models can be easily replicated and deployed multiple times in a production setting.
Who is the course for?
The course is designed for a variety of individuals in different roles. Whether you're a data scientist who is new to ML or someone who has deployed a few models within their organization, this course is perfect for anyone looking to learn more about best practices in model deployment.
Specifically, this course is for:
- Individuals who have built their first ML models and want to know how to deploy them into an API or production environment
- Data scientists looking to learn more about best practices in model deployment
- Software developers who want to expand their knowledge into fully integrated machine learning pipelines
What else should you know?
It's important to note that while this course covers the basics of model deployment and offers hands-on examples of how to deploy a model, there is much more to model deployment than what is covered in the course.
Topics like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow are not covered in the course.
That being said, the course aims to provide individuals with a comprehensive overview of the research, development, and deployment lifecycle of an ML model. Once individuals have a strong foundation in the basics of model deployment, they'll be well-equipped to explore more advanced topics on their own.
Course Rating and Reviews
The Deployment of Machine Learning Models course has a course rating aggregate of 4.39364 out of 5 and 4,672 reviews, making it one of the top-ranked courses for model deployment available online.
Reviewers frequently praise the course for its comprehensive curriculum and engaging video tutorials. Many reviewers also note that the hands-on examples of Python code offered in the course were particularly helpful for their own projects.
Ultimately, the Deployment of Machine Learning Models is an excellent course for anyone looking to learn how to deploy their ML models in production. The course covers everything from the basics to more advanced concepts, and provides hands-on examples of how to transform Jupyter notebooks into production code.
