The Easy Way for AI: AWS SageMaker
In the course, services and methodologies for developing applications based on Machine Learning will be covered. Students will be taken from defining the problem to solve with the help of ML, learn how to fragment it and prepare databases on which to do model training. The course also focuses on using AWS with Amazon SageMaker.
CODE: DSAI107
Category: Artificial Intelligence Course
Teaching Methodology
The course includes educational laboratories in which each student will be able to carry out training exercises that will provide practical experience in the use of the tool, for each of the topics covered during the course.
Prerequisites
- Basic knowledge of AWS.
- Basic knowledge of Python.
- Basic knowledge of statistics.
Below is an overview of the course contents:
- Introduction to Machine Learning
- Preparing a DataSet
- Training a Model
- Evaluating and Tuning a Model
- Deploying a Model
- Challenges in Ops
- Other Model Creation Tools
At the end of the course, participants will be able to:
- Discuss the benefits of different types of machine learning for solving business problems.
- Describe the typical processes, roles, and responsibilities of a team that builds and deploys ML systems.
- Explain how data scientists use AWS tools and ML to solve a common business problem.
- Summarize the steps a data scientist takes to prepare data.
- Summarize the steps a data scientist takes to train ML models.
- Summarize the steps a data scientist takes to evaluate and fine-tune ML models.
- Summarize the steps involved in deploying a model to an endpoint and generating predictions.
- Describe the challenges in Ops for ML models.
Duration – 1 day
Delivery – in the classroom, on site, remotely
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
- Instructor: English
- Workshops: English
- Slides: English
