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Custom MLOps Platform and AI Integration for Higher Education

Services Covered:

AI and Machine Learning • Custom Software Development
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Industry
Industry

Education

Duration
Duration

11 months

Budget
Budget

200K - 999K

Start Date
Start Date

1 March 2023

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Offshore

Project Capability Score (PCS)

gauge meter
75/100
Good

Project Capability Score (PCS) estimates how capable Addepto is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

Western Governors University

mapSalt Lake City, United States of America

Project Summary

Addepto designed and developed a custom platform for an online higher education institution to integrate machine learning methodologies and MLOps, enabling AI models to be production-ready without rewriting code. The platform served as a foundational template embedding MLOps best practices, automated processes, and Notebook integration, resulting in measurable time savings and immediate operationalization of models. The team demonstrated deep technical understanding and a business-oriented approach.

Key Challenges

  • Smooth the deployment process and minimize discrepancies between development and production environments
  • Expedite transition from concept to full-scale production
  • Make AI models production-ready without requiring code rewriting or additional testing

Project Deliverables

  • Foundational MLOps platform template
  • Built-in MLOps best practices for standardized project setup
  • Automated deployment and update processes
  • Notebook integration with project configurations
  • Flexible, modifiable workflows implementing MLOps stages

Project Solution

Addepto designed a platform that integrates Data Science experimentation with MLOps methodologies so AI models are production-ready without code rewrites. They built a foundational template for new projects embedding built-in MLOps best practices, set up automated processes and updates to reduce manual effort, integrated Notebooks with project configurations for seamless workflows, and introduced flexible, configurable MLOps stages (preliminary data processing, automatic verification, testing) via modifiable configuration files. Technologies used included SQL, Python, Databricks, and MLFlow.

Project Outcome

  • Time savings from AI models being immediately operational in business settings
  • Data scientists could deploy models without rewriting code or additional testing
  • Streamlined deployment process reducing discrepancies between dev and production

Platforms

  • AI/ML PlatformAI/ML Platform
  • CloudCloud

Tech Stack

  • PythonPython
  • SQLSQL
  • Azure DataBricksAzure DataBricks

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Addepto delivered a platform with AI models that resulted in time savings. The team addressed the client's core problem and went above and beyond to understand the client's tech stack. Their technological expertise and state-of-the-art technologies complemented their business-oriented approach.”

Michelle Medeiros

Sr Director of Data & ML

Note: This endorsement is based on publicly available client feedback from external review sources.

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