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openHPI is a free, open learning platform from the Hasso Plattner Institute, offering courses on IT-Technologies and digital transformation.
Learn how column-oriented in-memory databases work: dictionary encoding and compression, insert-only updates, differential buffer and merge, aggregation on the fly, logging and recovery.
Learn the foundations of parallel programming: semaphores and actors, shared and distributed memory parallelism, accelerators, task parallelism, message passing, and proven patterns.
Model business processes and decisions with the BPMN and DMN standards, analyze process model behavior, then simulate processes in Signavio and execute them in Camunda.
Learn how the Internet works end to end: LAN and WAN transmission, IPv4 and IPv6 internetworking, TCP and UDP transport, applications, plus hands-on Wireshark exercises.
Learn Semantic Web fundamentals: represent knowledge with ontologies, apply formal knowledge representation on the web, and access Linked Data and the Web of Data.
Model business processes with the BPMN standard and describe operational decisions with DMN, learning each modeling element's precise meaning, decision structure, and decision logic.
Learn to represent knowledge for the Semantic Web: identify things with URIs and RDF, query RDF(S) with SPARQL, and explore applications in the Web of Data.
Learn efficient AI methods that cut energy use: deep learning model compression, low-bit quantization for large language models, collaborative inference, and open-source tools and models.
Examine the paradox of AI and sustainability: how AI drives efficiency and green innovation while consuming energy in model training, plus practical recommendations for sustainable AI development.
Get an overview of enterprise applications through SAP: its history, business processes, ERP modules such as Financials, Supply Chain Management and CRM, plus enterprise cloud platforms.
Examine how to cut the energy use and carbon footprint of digitalization, applying algorithmic efficiency and sustainability by design across data centers, networks, cloud, AI and mobile devices.
Develop energy-efficient software: apply algorithmic efficiency and green testing, measure the CO2 emissions and energy use of applications and LLMs, and run data centers efficiently.
Build and fine-tune computer vision models in PyTorch through coding demos and projects covering image classification, object detection, segmentation, and generative modeling.
Learn how text becomes numbers: explore tokenization, historical and modern embedding models, and practical applications of text embeddings without prior AI experience.
Assess the environmental cost of AI: measure the carbon footprint of AI models, apply energy and carbon accounting, and work through impact-estimation case studies.
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