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AI/ML Model Development & Documentation

Active

State & Local

Response Deadline10/30/2026, 04:00:00 PM UTC

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

AI Contract Overview

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This subcontract opportunity involves the development and documentation of artificial intelligence and machine learning models designed for traffic data analysis. Working for prime contractors on Michigan Department of Transportation projects, the selected provider will utilize high-compute GPU environments and frameworks such as PyTorch or TensorFlow to analyze traffic metrics and trends. The primary deliverables include fully trained models and comprehensive Model Cards that detail the context, performance, and limitations of each model. This project is managed under the DTMB for the state of Michigan, with a response deadline set for October 30, 2026.

General Info

Agency

Michigan → DTMB

NAICS

541511 - Custom Computer Programming Services

Place of Performance

MI, USA

Set-Aside

NONE

Documents

This scope was carved out of 260000002471.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

RFP-171-260000002471-1 | Transportation Data Management System RFP

AI Contract Breakdown

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Timeline

PostedOct 2

subcontract

Response DeadlineOct 30

Submission deadline

Response Deadline

10/30/2026, 04:00:00 PM UTCin 24 days

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Organization & Contact Information

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AgencyMichigan → DTMB

ContactsNo contacts available

OfficeN/A

Organization / AgencyMichigan → DTMB

Office AddressN/A

ContactsNo contact information available

Full Description

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Develops and documents machine learning or artificial intelligence models for traffic data analysis for prime contractors on Michigan Department of Transportation (MDOT) projects. Creates AI/ML models to analyze traffic metrics and trends using frameworks like TensorFlow or PyTorch in high-compute GPU environments. Produces Model Cards explaining context, performance, and limitations. Delivers trained models and accompanying Model Cards.

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