Lesson Library

DEEP Inspection for Materials Science

Learn about visual inspection, object detection, and instance segmentation in materials science using deep learning methods.

ai materials_science deep_learning

difficulty: intermediate maturity: stable

Generative AI for Life Science

Learn about generative AI applications in life sciences, including cell fate prediction with autoencoders, protein language models, and vision language models for medical imaging.

ai gen_ai life_science machine_learning

difficulty: intermediate maturity: alpha

Hackathons

Participate in Sweden AI Factory hackathons and collaborative coding events

programming ai

difficulty: intermediate maturity: stable

Hands-on Kubernetes Workshop

Materials for the Kubernetes workshop @ Sweden AI Factory / ENCCS. Learn to deploy and manage containerized applications on Kubernetes.

cloud_computing devops

difficulty: intermediate maturity: beta

Introduction to Agentic Coding

Introduction into agentic coding, agent harnesses, context management, tool-calling, MCP.

ai agentic gen_ai programming

difficulty: intermediate maturity: stable

Introduction to Deep Learning

Deep learning is a powerful subset of machine learning where computers learn patterns from data, similar to how our brains learn. It uses artificial neural networks - systems inspired by biological neurons that process information through many layers. This beginner-friendly workshop, organized by the Sweden AI Factory in partnership with LUMI AI Factory, provides an introduction to deep learning concepts, workflows, architectures, and practical applications.

ai programming

difficulty: beginner maturity: stable

Introduction to MLOps

MLOps (Machine Learning Operations) combines machine learning, software engineering, and DevOps to reliably build, deploy, monitor, and maintain ML models in production. This 3-half-day event covers the entire MLOps pipeline with interactive lessons and hands-on labs.

mlops machine_learning devops

difficulty: intermediate maturity: beta

Introduction to PyTorch distributed training frameworks

Learn parallel computing techniques with PyTorch for distributed training across multiple GPUs.

ai hpc pytorch

difficulty: advanced maturity: alpha

Julia for high-performance data analytics

Julia is a modern programming language offering high performance comparable to C and Fortran without sacrificing simplicity. This lesson covers data formats, DataFrames, linear algebra, data science, machine learning, regression, and time-series prediction.

julia data_science hpc programming

difficulty: intermediate maturity: stable

LLM fine-tuning - Theory and practice

Large language models (LLMs) are now ubiquitous in many applications. However, it can be the case that an off-the-shelf model does not perform well in a specific task, or that we would like a smaller model focused on some specific use cases and avoid expensive, large general-purpose models. In these cases, we need to find a way to “tweak” the behaviour of a model to fit the requirements. This beginner-friendly workshop, organized by the Sweden AI Factory, provides an introduction to LLMs and how their behaviour can be modified.

ai programming

difficulty: intermediate maturity: stable

Multi-GPU AI Train the Trainer Workshop

5-day workshop covering multi-GPU AI training, including PyTorch Distributed Data Parallel, model parallelism, PyTorch Lightning, fine-tuning neural networks, computer vision, MLOps on HPC, Ray, RAG, and hyperparameter tuning.

ai hpc pytorch

difficulty: advanced maturity: beta

Responsible Use of Generative AI in Assisted Coding

A practical framework for researchers who want to use AI coding assistants responsibly. Covers three scenarios of increasing automation: chat-based coding, IDE integration, and full agentic code development.

ai gen_ai programming

difficulty: beginner maturity: beta

The Marvelous Guide of Supercomputing

Introduction to fundamentals of HPC, AI and Cloud in an accessible language.

ai cloud_computing hpc

difficulty: beginner maturity: stable