High Level Education Path for MLOps

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 IT and cloud computing.

Below is an overview of the course contents: Day 1 (Dev Side):

1. Python: This module offers a comprehensive overview of the Python programming language, covering both basic and advanced concepts needed for application development.
2. Execution environments: Students learn to configure and manage execution environments for developing and testing their applications, including virtual environments and development configurations.
3. Git: This module provides practical guidance on using Git, a widely used version control system, for tracking changes to source code and collaborating on software development.< /li>

Day 2 (Infrastructure Side):

1. Cloud: This module introduces the fundamental concepts of cloud computing and illustrates the use of cloud platforms such as Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform for the distribution and management of applications.
2. Docker: Students learn to use Docker, a containerization platform that allows you to build, deploy, and manage applications in lightweight, portable software containers.
3. Kubernetes: This module provides an in-depth overview of Kubernetes, an open-source system for automating the deployment, scaling, and management of containerized applications in cloud and on-premise environments.< /li>

Day 3 (Operations Side):

1. AI Fundamentals with Python: Students are introduced to the fundamental concepts of artificial intelligence using the Python programming language, including popular libraries such as TensorFlow and PyTorch.
2. ML Basics with Python: This module provides an overview of the basic concepts of machine learning using Python, covering topics such as model training, performance evaluation, and feature selection .
3. Pipeline Basics on Kubeflow: Students learn the basic concepts of pipelines on Kubeflow, an open-source framework designed to simplify the development, training, and deployment of machine learning models on Kubernetes.

At the end of the course, participants will be able to:

• Understand the MLOps Engineer role.
• Understand the fundamental concepts of AI
• Understand the fundamental concepts of ML
• Understanding Cloud environments.
• Understand containerization.
• Understand pipelines and their uses.

Duration – 3 days Delivery – in the classroom, on site, remotely PC and SW requirements:

• Internet connection
• Web browser, Google Chrome
• Zoom

Language

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