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    • Calibrated confidence estimator
    • Inference scaling
    • Rare node co-activations in error detection
    • Continuous data monitoring and cleaning dashboard for smart meters
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    • Privacy-friendly Image Preparation for TinyML
    • Model cards toolkit
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    • OSS MLOps platform 
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IML4E – News

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IML4E at DIN/DKE NA 043-01-42 GA
Date: Fri., 14. Jan. 2022
Jürgen Großmann, project coordinator of IML4E, presented the IML4E project in the DIN/DKE NA 043-01-42 GA meeting on Thursday January 13th 2022. The standardization committee DIN/DKE NA 043-01-42 – Artificial Intelligence is the national standardization body with the task to mirror the standardization activities of ISO/IEC JTC 1/SC 42 as well as the CEN/CENELEC Focus Group on AI. DIN/DKE NA 043-01-42 is responsible to bring the German opinion into the committees of ISO and CEN/CENELEC and to develop proposals for standardization topics on all levels.
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IML4E at Eclipse AICE OpenLab meeting
Date: Wed., 24. Nov. 2021
Today, Fraunhofer researcher Jürgen Großmann, coordinator of the IML4E project, participated in the Eclipse AI, Cloud & Edge (AICE) meeting in Brussels. The AICE OpenLab WG manages and operates an open lab that provides a range of resources to promote the further development, implementation, and verification of open source software for AI, cloud, and edge computing. The working group meeting highlighted the benefits and role that open source and open platforms will have for sustainable industrial adoption of AI and ML. Presentations from AI4EU, Redhat, Mindspore, Huawei and others presented ideas and goals for their contribution to building a community-based cloud and AI platform especially considering an open source perspective. If you are interested in the Eclipse AI, Cloud & Edge (AICE) working group, check out their website. IML4E looks forward to working with the AICE Open Lab in the future.
Konferenz mit Publikum
IML4E presents itself at ICTSS 2021
Date: Mon., 15. Nov. 2021
In a Project Reports Session, Jürgen Großmann, as coordinator of the IML4E project, presented the project goals and project innovations of the IML4E project to a wider audience. The subsequent discussion provided interesting opportunities for cooperation with other research projects and interested scientists.
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The Finnish partners receive funding
Date: Fri., 12. Nov. 2021
After a thorough review and a little later than expected, we have received the good news that the Finnish Consortium will be funded. This ensures the participation of Basware, Granlund, Reaktor, Silo AI, and the University of Helsinki in the IML4E project. IML4E now includes 12 partner organisations from industry and academia.
Logo IML4E Projekt
iml4e.org online
Date: Thu., 11. Nov. 2021
By launching the iml4e.org website, we are starting our main channel for communicating the current project progress and results in IML4e. The site iml4e.org will report on the planned project activities, make the public project delivarebles and publications visible and provide news to all interested followers of the project.
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Innovations and Objectives
  • Objectives
  • IML4E innovations
Case Studies
  • Basware
  • Granlund
  • Reaktor
  • Vitarex
  • Siemens
Results
  • MLOps monitoring platform
  • MLOps Framework 
  • Adversarial Test Toolbox
  • ML-Py-Stevedore
  • Data Quality Dashboard
  • ML Lineage
  • Mosquito data cleaner
  • MLOps Testing Methodology
  • Discrepancy Scaling for Unsupervised Anomaly Detection and Localization
  • Autonomously Adaptive Experimentation-Driven Pipeline
  • VALICY – a virtual validation system for AI/ML and complex software applications
  • SAGED: Error Detector for Tabular Data
  • Cost-effecient ML
  • ML Metrics Typology + AI Ethics Metrics
  • Calibrated confidence estimator
  • Inference scaling
  • Rare node co-activations in error detection
  • Continuous data monitoring and cleaning dashboard for smart meters
  • MLOps Maturity Assessment Scheme
  • Privacy-friendly Image Preparation for TinyML
  • Model cards toolkit
  • Pipeline Probe
  • Data Quality Evaluation Tool
  • OSS MLOps platform 
  • CABC for MLOps
  • Validation of pose estimation models
  • ML-Py-Stevedore
Resources
  • Conferences and Publications
  • Deliverables
Up to date
  • News
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  • Countries
  • Partners

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