High Level Education Path for MLOps
The course provides a high-level overview of the fundamental principles of MLOps, emphasizing both technical and operational aspects. Divided into three days of training, the course covers aspects of development, infrastructure and operations in the context of operational machine learning.
CODE: DSAI111
Category: Artificial Intelligence Course
DESCRIPTION
COURSE CONTENT
COURSE OBJECTIVES
ADDITIONAL INFORMATION
DESCRIPTION
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.
COURSE CONTENT
Below is an overview of the course contents:
Day 1
- Python Basics for Machine Learning
- Python Virtual Envinronment
- Git for versioning
Day 2
- Cloud for MLOps
- Docker for MLOps
- Kubernetes for MLOps
Day 3
- Basics of AI with Python
- Basic elements of ML with Python
- Elementary concepts of Pipeline on Kubeflow
COURSE OBJECTIVES
At the end of the course, participants will be able to:
- Understand the figure of the MLOps Engineer.
- Compare and contrast MLOps and DevOps.
- Understand the basic concepts of AI, ML.
- Acquire elementary knowledge of Cloud services in MLOps.
- Gain elementary knowledge of containerization.
Understand the basics of Kubernetes. - Understand pipelines and their use.
- Understand the basics of Kubeflow in the context of MLOps.
ADDITIONAL INFORMATION
Duration – 3 days
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
ARTIFICIAL INTELLIGENCE COURSE CATALOG
ARTIFICIAL INTELLIGENCE COURSE CATALOG
