Prior Information Notice: Monitoring Service

FinlandTenders notice for Prior Information Notice: Monitoring Service. The reference ID of the tender is 116363622 and it is closing on 02 May 2025.

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Tender Details

  • Country: Finland
  • Summary: Prior Information Notice: Monitoring Service
  • FIT Ref No: 116363622
  • Deadline: 02 May 2025
  • Competition: ICB
  • Financier: Self Financed
  • Purchaser Ownership: Public
  • Tender Value: Refer Document
  • Notice Type: Tender
  • Document Ref. No.: 175259-2025
  • Purchaser's Detail :
  • Purchaser : STATISTICS FINLAND, TILASTOKESKUS
    Address: Tyƶpajankatu 13
    Town: Helsinki
    NUTS-Code: FI1B1
    Postal Code: 00580
    Attn: Hansel Oy
    Phone: +358 294444300
    Email :hankinnat@stat.fi
    URL :http://www.stat.fi

  • Description :
  • Statistics Finland is acquiring a cloud-based Machine Learning (ML) monitoring service to enhance trust and reliability in its machine learning operations. This service will complement an existing ML platform built on Azure Databricks. Objectives: Actively identify and manage degradation in ML models, ensuring high-quality and trustworthy outputs for official statistics. Support a growing community of internal users in their daily work, including statistics experts with varying levels of ML and data science experience. The service should either integrate directly with our existing Azure environment or, alternatively, be set up as a separate service within our own Azure tenant. In either case, the solution must ensure that no data leaves the desired geographic region (EU) and that all data security and protection requirements for potentially sensitive data are fully addressed. Scope of Acquisition: Licensing for a cloud-based ML monitoring service capable of initially supporting approximately 10 machine learning models, with scalability to accommodate an increasing number of models over time. Technical support and expert training for efficient platform use. Tools and metrics for: - comparing model predictions with realized ground truth results across a variety of ML problems (e.g. classification, regression) - monitoring various types of drifts in input data (e.g. covariate drift, concept drift, novel values) - evaluating model performance even in the absence of ground truth M...
  • Documents :
  •  Tender Notice

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