Курси з ІТ та інновацій. Від експертів. Безкоштовний. У власному темпі.

Digital systems offer a great opportunity to significantly reduce carbon emissions and can contribute to the efficient use of energy. However, all systems also need energy. This area of tension is addressed in the course: Sustainability in the digital age - Energy-Efficient Software Development. To effectively reduce the carbon footprint of digitalization, it is necessary to apply algorithmic efficiency and sustainability by design as guiding principles in digital engineering. We will introduce strategies to develop software that prioritizes minimized energy consumption through optimal coding and green testing practices. We will look at how the CO2 emissions of operating software applications can be measured and calculated. How to measure the performance and energy consumption of Large Language Models will be covered as well. Further we share approaches how to use advances in hardware technology and operate digital systems efficiently in data centers based on eco-friendly and cost-effective capacity management strategies.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Durch ein geheimes Auswahlverfahren wurde Euer Team auserwählt, in die dunklen Gassen von Berlin und Potsdam einzutauchen - zur Infiltration des Verstecks einer berüchtigten Hacker-Organisation. Eure Mission: Unmask the Underground! :male-detective: Schon beim ersten Schritt wird Euch klar: Hinter der Fassade verbirgt sich eine große kriminelle Infrastruktur, die es zu durchbrechen gilt.

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Willkommen zu den 3. Potsdam Cyber Games. Mögen die Spiele beginnen.

Practical Computer Vision in PyTorch is a comprehensive, hands-on course designed for developers and practitioners eager to explore computer vision using PyTorch. The course covers a wide spectrum of tasks, ranging from image classification and object detection to segmentation and generative modeling. With a strong emphasis on hands-on implementation, the participants will engage in coding demos and build projects using industry-standard tools and libraries. By the end of the course, the participants will be equipped with the skills to build, fine-tune, and deploy computer vision models for real-world applications.

In an era where digital technologies are reshaping industries and daily life, the environmental impact of AI systems has become a growing concern. This course explores efficient AI methodologies to address these challenges. From deep learning model compression to low-bit quantization and collaborative inference, we delve into techniques that enhance computational efficiency and reduce energy consumption. In Week 2, we focus on low-bit quantization specifically for large language models (LLMs), showcasing cutting-edge open-source tools and models. Join us to learn how to build sustainable AI systems while pushing the boundaries of innovation.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

This course explores the critical  intersection of AI and sustainability, highlighting how technological advancement can align with environmental responsibility. It offers insights into the dual nature of AI, noting its potential for increasing efficiency and driving sustainable innovation, while also acknowledging the significant energy consumption involved in training and applying AI models. In this course, we are not only examining the paradox of AI and sustainability but also provides actionable recommendations for incorporating sustainability into AI application development. By examining AI's transformative role and its environmental impact, the course offers a comprehensive understanding of how AI can be harnessed to support global sustainability goals effectively.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.