The Easy Way for AI: AWS SageMaker

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.

1. Introduction to Machine Learning: We will begin by exploring the basics of ML, understanding its principles, and discovering its various applications in real-world scenarios.
2. Preparing a Dataset: You will learn how to collect, pre-process and clean data to create a high-quality dataset, which is critical for training accurate ML models.
3. Evaluate and Optimize the Model: We will delve into the techniques used to evaluate the performance of ML models and you will learn how to optimize their parameters to obtain optimal results.
4. Deploying a Model: We will explore the process of deploying ML models in production environments, making them accessible to end users for real-time predictions.
5. Operational Challenges in Machine Learning: You will gain insights into the operational challenges that arise when implementing large-scale ML solutions and learn how to effectively overcome them.
6. More Model Building Tools: You will discover additional tools and libraries that can help you create, train, and evaluate ML models, expanding your ML development toolkit.

• Discuss the benefits of different types of Machine Learning for tackling a problem.
• Describe the typical steps, roles and responsibilities in a team that creates and deploys ML models.
• Explain how data scientists use AWS and services to solve some problems through ML.
• Describe the challenges to be faced in the ML world on the operational side.
• Understand which AWS tools to use and what function they perform in the world of Machine Learning.

• Internet connection
• Web browser, Google Chrome
• Zoom

• Instructor: Italian
• Workshops: English
• Slides: English