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DTSTART;TZID=Europe/Stockholm:20260415T110000
DTEND;TZID=Europe/Stockholm:20260415T123000
DTSTAMP:20260419T165210
CREATED:20260224T074823Z
LAST-MODIFIED:20260306T135522Z
UID:10000011-1776250800-1776256200@mimer-ai.eu
SUMMARY:Operationalizing AI: MLOps x LLMOps
DESCRIPTION:About the webinar\nIn this MLOps and LLMOps webinar\, we’ll walk through the entire AI lifecycle – from idea and experimentation to production\, deployment and continuous monitoring\, highlighting how AI differs from traditional software (data-driven\, non-linear\, and sometimes unpredictable even when “done right”). You’ll learn the main deployment patterns (batch/offline\, real-time/online\, and common patterns employed in cloud solutions) and the key trade-offs around latency\, scaling\, and operational reliability. \nWe’ll then connect MLOps and LLMOps in a practical way: versioning data/models/prompts\, reproducibility\, CI/CD\, and testing strategies for probabilistic systems. It’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. \nWho is the webinar for? \nIt’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. Also suited to those with no experience in building and deploying AI models\, and are curious on AI/ML/LLM Ops. \nKey takeaways for participants: \n\n\nKey differences between AI and traditional software \n\n\nHow these differences translate to model deployment \n\n\nWhat is ML and LLM Ops and how they differ \n\n\nDifferent model deployment strategies \n\n\nSpeaker bio:\nMurilo Kuniyoshi Suzart Cunha (https://www.linkedin.com/in/murilo-cunha/) \nMurilo is a machine learning engineer specializing in productionizing models and applying AI Ops best practices\, with a focus on the evolving landscape of LLMOps. He takes a pragmatic approach to machine learning\, ensuring AI initiatives deliver tangible ROI. An experienced international conference speaker and open source supporter\, Murilo is also the host of the Monkey Patching Podcast.
URL:https://mimer-ai.eu/event/operationalizing-ai-mlops-x-llmops/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260325T170000
DTEND;TZID=Europe/Stockholm:20260325T200000
DTSTAMP:20260419T165210
CREATED:20260313T133240Z
LAST-MODIFIED:20260313T211947Z
UID:10000014-1774458000-1774468800@mimer-ai.eu
SUMMARY:Mimer hackathon
DESCRIPTION:​About the hackathon\nJoin our hackathon to learn how to use “research AI agents” using free European infrastructure. \nWe are pleased to collaborate with ​Stockholm AI and bring you this mini hackathon that will utilize Mimer’s free AI infrastructure. \n​The agenda is as follows: \n\n​17:15 Doors open + mingle\n18:00 Introduction to Mimer and the hackathon task. The task will be to create “research agents” using Mimer and open weight models that can answer questions using Sweden’s public data (e.g. SCB).\n​18:30 Hackathon begins\n​20:00 Wrap-up presentations and demos\n​20:30 Home time\n\n​Food and beverage will be provided. \n​NOTE: While the models will be served on Mimer infra\, people are still expected to bring their laptops to participate. You should have experience in setting up and running Python applications\,.e.g using uv\, calling APIs\, etc. You are of course free to use your favourite coding agent/IDE of choice. \nVenue\nDrottning Kristinas väg 61\, Stockholm
URL:https://mimer-ai.eu/event/agent-mimer-hackathon/
LOCATION:Drottning Kristinas väg 61\, Stockholm
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/03/Mimer-hackathon.jpg
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260317T100000
DTEND;TZID=Europe/Stockholm:20260326T150000
DTSTAMP:20260419T165210
CREATED:20260223T103811Z
LAST-MODIFIED:20260306T131947Z
UID:10000010-1773741600-1774537200@mimer-ai.eu
SUMMARY:CodeRefinery workshop on coding tools and techniques for reproducible research
DESCRIPTION:About the course\n\n\n\nAre you writing code for your research? Do you want to make your research results more reproducible? Do you struggle to reproduce results of your own or others computations? Join the CodeRefinery workshop on coding tools and techniques. \n🗓️ The workshop runs on March 17–19 and March 24–26\, offering three consecutive days each week to maximize learning and networking opportunities. 📍Hjärne and Lallerstedt\, KTH Library and online \n\n\n\n\n\nThe intended audience for this workshop are researchers of all domains\, levels and preferred programming languages who write code in their research\, and the aim is to improve the reproducibility of our research by deepening the knowledge of the tools that enable better code development and sharing. \n\n\n\nThe workshop is held online (streamed on Twitch) with hands-on sessions. \n\n\n\nThe event is free of charge. More info and registration on the CodeRefinery Workshop site. \n\n\n\nIn-person hybrid event\n\n\n\nIn addition to the option to participate online\, this edition of the workshop also offers limited seats to a local exercise group at KTH library for participants in Sweden. \n\n\n\nSecure your spots here: \n\n\n\n\nRegistration for week 1\n\n\n\nRegistration for week 2\n\n\n\n\nDuring the CodeRefinery workshop on coding tools and techniques you will get the opportunity to interact with trainers from Mimer\, ENCCS\, KTH and other partner organizations.
URL:https://mimer-ai.eu/event/coderefinery-workshop-on-coding-tools-and-techniques-for-reproducible-research/
LOCATION:Hjärne and Lallerstedt\, KTH Library and online
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/02/coder.jpg
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260303T110000
DTEND;TZID=Europe/Stockholm:20260303T120000
DTSTAMP:20260419T165210
CREATED:20260202T082811Z
LAST-MODIFIED:20260202T082811Z
UID:10000009-1772535600-1772539200@mimer-ai.eu
SUMMARY:Secure coding for data scientists
DESCRIPTION:About the webinar\nWe give an overview of secure coding practices and guidelines. What should we keep in mind while writing code so as to mitigate security concerns. We will mention and explain some of the most common vulnerabilities is code and exemplify from a data science perspective. \nWho is the webinar for?\nDevelopers and code producers who work with Python and data science and need to take security risks into account when coding. Beginners in cyber security and secure coding. \nKey takeaways for participants:\n\nWhat are common vulnerabilities in code and mistakes or bad practices?\nHow to write safe code in some typical cases.\nWhat are some useful resources such as guidelines and standards for secure coding?\n\nSpeaker bio:\nDavid Eklund is an AI researcher at RISE working with mathematical modeling as well as privacy and security of AI models. David has a PhD in mathematics from KTH the Royal Institute of Technology. He is one of the training coordinators of the MIMER AI factory. \nAbdul Ghafoor is an Application Security Specialist with over a decade of experience in secure application development and vulnerability assessment. He holds a Ph.D. in Application Security from KTH Royal Institute of Technology (Sweden)\, where his research focused on secure-by-design applications\, shift-left security\, and the use of AI in cybersecurity.
URL:https://mimer-ai.eu/event/secure-coding-for-data-scientists/
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260216T090000
DTEND;TZID=Europe/Stockholm:20260219T120000
DTSTAMP:20260419T165210
CREATED:20260109T090652Z
LAST-MODIFIED:20260109T122441Z
UID:10000006-1771232400-1771502400@mimer-ai.eu
SUMMARY:Introduction to Deep Learning
DESCRIPTION:Register latest by: 12 Feb 2026\nAbout the event\nThis event is organised by: Mimer AI Factory & LUMI AI Factory (CSC) \nDeep 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. The term “deep” refers to networks with tens or hundreds of layers\, each containing millions of connections. Deep learning today powers technologies ranging from foundational applications such as language models and image recognition\, to cutting edge applications such as weather forecasting and protein folding. However\, for beginners\, stepping into this field can feel daunting and we intend to make this easy for you. \nThis online workshop\, organized by Mimer\, in partnership with LUMI AI Factory\, is designed to provide a beginner-friendly introduction to deep learning concepts\, workflows\, architectures\, and practical applications. You will learn end-to-end approaches for: \n\n\ntackling AI tasks including classification and regression \n\n\nbuilding deep model architectures such as Convolutional Neural Networks (CNNs) \n\n\napply advanced training techniques such as transfer learning \n\n\nWho is this for? \n\nStudents and early-career researchers in computer science\, bioinformatics\, natural sciences\, engineering\, or related fields\nData scientists working on deep learning-based applications\nAspiring software developers looking to learn foundational skills in AI\n\nKey takeaways for participants: \nA gentle introduction to deep learning fundamentals\, covering: \n\nCore concepts and terminology\n\nSteps in a deep learning workflow using Python and Keras (with Tensorflow\, and potentially also with PyTorch as backend) \n\nData preparation for training\nImplementing a basic neural network\nMonitoring and troubleshooting the training process\nVisualizing results and evaluating model performance\n\nPrerequisites: \n\n\n\nBasic Python programming skills and familiarity with packages like NumPy\, Pandas\, and Matplotlib\nExperience working with Jupyter notebooks (recommended but not mandatory)\n\n\n\nSchedule\nAll times in CET (Europe/Stockholm time) \n\n\n\nDay\nTime\nContents\n\n\n\n\n2026-02-16\n9:00 – 11:00\nSetup and dry-run\n\n\n2026-02-17\n9:00 – 12:00\nIntroduction;\nClassification by a neural network using Keras\n\n\n2026-02-18\n9:00 – 12:00\nMonitoring the training process\n\n\n2026-02-19\n9:00 – 12:00\nAdvanced Layer types; Transfer learning\n\n\n\nInstructors and Teaching Assistants\n\nAshwin Mohanan\, Mimer AIF / RISE\nFrancesco Fiusco\, Mimer AIF / RISE\nKatja Mankinen\, LUMI AIF / CSC\nLodovico Giaretta\, Mimer AIF / RISE\nMarlon Tobaben\, LUMI AIF / CSC\nOskar Taubert\, LUMI AIF / CSC\nYonglei Wang\, Mimer AIF / NAISS
URL:https://mimer-ai.eu/event/introduction-to-deep-learning/
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/01/Introduction-to-deep-learning.jpg
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260211T113000
DTEND;TZID=Europe/Stockholm:20260211T133000
DTSTAMP:20260419T165210
CREATED:20251120T135457Z
LAST-MODIFIED:20260109T105307Z
UID:10000005-1770809400-1770816600@mimer-ai.eu
SUMMARY:Healthcare foundation models
DESCRIPTION:| Speaker: Ludvig Hult\, Uppsala University \nAbout the Webinar\nAll of society is in rapid change as AI becomes more prolific. Generative models for text and images have disrupted sectors like marketing\, publishing and software. Healthcare seems to be lagging behind\, largely because many obstacles for AI in healthcare are poorly addressed by well known commercial providers like ChatGPT and Gemini. \nThis webinar highlights the unique challenges in healthcare\, such as data security and model safety. Topics covered include applications of AI in healthcare (transcription\, summarization)\, and key challenges (data privacy\, algorithmic fairness). \nThe EmergAI project at Uppsala University explores other types of AI to face these challenges. One of those models\, the healthcare event foundation model is introduced\, which can be thought of as a specialized LLM. Other approaches such as use of multimodal models in this context are also presented. \nWho is the webinar for\nThis talk is ideal for healthcare professionals\, data scientists\, researchers in life sciences\, and those in related fields. Additionally\, it offers inspiration to anyone with a general interest in AI and machine learning\, highlighting the importance of developing specialized models to ensure safe and reliable systems. \nKey takeaways for participants:\n\nExplore how AI can be employed in transcription\, summarization\, and image recognition within healthcare settings.\nUnderstand the key challenges facing AI implementation in healthcare\, including data limitations\, security concerns\, safety issues\, fairness considerations\, and compliance with Medical Device Regulations (MDR).\nDiscover EmergAI’s innovative approach through the healthcare event foundation model\, multimodal models and other specialized models\, including applications in ECG analysis and self-reported data.\nUnderstand the importance of tailored AI models like sequence models in healthcare and how they compare to generic large language models (LLMs).\n\n 
URL:https://mimer-ai.eu/event/healthcare-foundation-models/
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260122T110000
DTEND;TZID=Europe/Stockholm:20260122T123000
DTSTAMP:20260419T165210
CREATED:20251120T071656Z
LAST-MODIFIED:20260123T092528Z
UID:10000003-1769079600-1769085000@mimer-ai.eu
SUMMARY:Resource-Efficient AI Model Parallelisation on LUMI Supercomputer
DESCRIPTION:| Speaker: Dr. Vijeta Sharma \nAbout the Webinar\nThis webinar explores how to harness the full potential of the LUMI supercomputer for large-scale AI model training through efficient utilisation of HPC resources. Participants will learn how thoughtful design of neural network architectures and optimal use of parallelisation techniques—such as model\, data\, and tensor parallelisation—can significantly improve performance and resource efficiency. \nThe session will demonstrate how frameworks like PyTorch and TensorFlow can be leveraged to distribute training workloads effectively across multiple GPUs and nodes on LUMI. Attendees will gain practical insights into balancing computational loads\, minimising communication overhead\, and achieving scalability for advanced AI workloads in an HPC environment. \nWho is the Webinar For\nThis webinar is designed for AI practitioners\, computational scientists\, and HPC users who aim to train large-scale machine learning models efficiently on modern supercomputing infrastructures. It is ideal for professionals seeking to optimise their deep learning workflows by leveraging advanced parallelisation techniques and maximising GPU performance on systems like LUMI. Participants with a background in AI\, data analytics\, or scientific computing who wish to scale their models and improve training efficiency in high-performance environments will particularly benefit from this session. \nKey Takeaways\n\nUnderstand the fundamentals of model\, data\, and tensor parallelisation.\nLearn strategies for efficient AI training on HPC systems like LUMI.\nExplore practical examples using PyTorch and TensorFlow.\nGain insights into optimising GPU utilisation for scalable AI workloads.
URL:https://mimer-ai.eu/event/resource-efficient-ai-model-parallelisation-on-lumi-supercomputer/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20251211T120000
DTEND;TZID=Europe/Stockholm:20251211T133000
DTSTAMP:20260419T165210
CREATED:20251119T184051Z
LAST-MODIFIED:20251210T103000Z
UID:10000002-1765454400-1765459800@mimer-ai.eu
SUMMARY:Profiling AI workloads on GPUs – Identifying performance improvements
DESCRIPTION:| Speakers: \n\nJoakim Stenberg\nRasmus Larsson\nDaniel Gustafsson\nEmelie Wahlström\n\nfrom AMD Silo AI \nAbout the webinar\nThe seminar is for participants to learn how to identify and analyze performance improvements for AI workloads using profiling. Understand the fundamentals of profiling for AI workloads\, which can be further applied upon completion of the seminar. The seminar combines theoretical knowledge with guided coding walkthrough to help participants identify performance improvements\, understand traces\, improve performance\, and enhance efficiency of AI workloads on GPUs. \nWho is the webinar for?\nIf you’re interested in how to use profiling to identify performance improvements for your AI workloads\, then this seminar is for you and/or if you’re working as e.g.\, a Data/AI scientist or as an engineer. \nKey takeaways for participants\n\nUnderstand how to identify and analyze performance improvements for AI workloads on GPUs\nDefine profiling and explain key terminologies\nUnderstand why profiling is important and its connection to performance
URL:https://mimer-ai.eu/event/profiling-ai-workloads-on-gpus-identifying-performance-improvements/
LOCATION:Online
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