About This Course
In this course, you will learn how to build, train, and deploy a machine learning model with Amazon SageMaker using the XGBoost ML algorithm. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
Taking ML models from conceptualization to production is typically complex and time-consuming. You have to manage large amounts of data to train the model, choose the best algorithm for training it, manage the compute capacity while training it and then deploy the model into a production environment. Amazon SageMaker reduces this complexity by making it much easier to build and deploy ML models. After you choose the right algorithms and frameworks from the wide range of choices available, SageMaker manages all of the underlying infrastructure to train your model at a petabyte scale, and deploy it to production.
In this course, you will learn how to:
- Create a SageMaker notebook instance
- Prepare the data
- Train the model to learn from the data
- Deploy the model
- Evaluate your ML model’s performance
The lab environment is available for the specified duration and will be required to complete the labs within the mentioned time. The lab environment can only be activated once.