Applied Edge AI: Deep Learning Outside of the Cloud

Course
  • Big Data and AI
  • Cloud and Operating Systems

Applied Edge AI: Deep Learning Outside of the Cloud

Compared to Cloud Computing, which is centralized in computing and data storage, Edge Computing brings computation and data storage closer to data sources.

Edge AI combines edge computing and AI technology and has become a rapidly developing field in the past few years. Edge AI enables AI computing directly on the edge or client device, enhancing power efficiency, supporting low latency, and solving data privacy problems.

Therefore, what improvements need to be made to traditional deep learning algorithms in Edge AI scenarios? This course teaches you about deep model compression and optimization techniques, decentralized and collaborative deep learning approaches and algorithms, software, and hardware for Edge AI.

Offered by PD Dr. Haojin Yang, Joseph Bethge (Teaching Team) | Last updated on Aug 31, 2026 | 3563 enrollments

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  • Intermediate
  • EN
  • Certificate
Course information

What will you learn?

  • Summary of deep learning basics relevant for this course
  • Deep model compression and optimization techniques
  • Decentralized and collaborative deep learning
  • Algorithms, software and hardware for Edge AI

Is this course for me?

Prerequisites

  • High school math is required (pre-course)
  • Basics in Machine Learning and Deep Learning
  • Python as programming language

The German MOOC Praktische Einführung in Deep Learning für Computer Vision teaches relevant basics regarding Deep Learning and Computer Vision. Unfortunately we did not find a fitting English alternative.

Level

Advanced

Time required

6-8 hours per week

How do I get started?

Environment setup (or what you'll need for our practical examples)

  • A Webbrowser (preferably on a laptop or desktop computer)
  • A Kaggle account (we will do all coding exercises using Kaggle Notebooks)

Attention: This course is currently in self-study mode, in which you do not have access to graded assignments/exams. Therefore, we can only issue you a certificate of participation.

Requirements for Proofs of Achievement
  • Confirmation of Participation: You will automatically receive a free Confirmation of Participation once you have completed at least 50% of the content.
  • Certificate: You will receive your official openHPI Certificate by earning at least 50% of the maximum number of points from all graded assignments.

Find out more on our page for Proofs of Achievement and Guidelines.

Our teachers

PD Dr. Haojin Yang

Haojin Yang is a senior researcher and multimedia and machine learning (MML) research group leader at Hasso-Plattner-Institute (HPI). Since 2019, he has been habilitated for a professorship. His research focuses on efficient deep learning, model acceleration and compression, and Edge AI.

Joseph Bethge (Teaching Team)

Joseph Bethge is a PhD student studying deep learning techniques in the area of Computer Vision with a focus on Binary Neural Networks (BNNs) and is one of the main authors of the Open-Source framework BMXNet 2 for BNNs.