Fragmentation Across ML Development and Deployment
Western Governors University (WGU) is a leading educational institution recognized for providing flexible, affordable, and accessible online education across fields like IT, business, healthcare, and teaching.
As AI initiatives expanded, WGU faced growing challenges in operationalizing machine learning models consistently across development and production environments.
- Disconnected experimental and production workflows: Models developed in notebooks and exploratory environments required manual handoffs before deployment, creating dependency issues and inconsistent results.
- Extensive code refactoring before deployment: ML models often needed significant restructuring and re-validation to meet production and MLOps requirements, slowing delivery cycles and increasing engineering overhead.
- Lack of standardized and automated deployment processes: The absence of a unified MLOps workflow made it difficult to scale collaboration between Data Scientists, Analysts, and MLOps Engineers while maintaining model integrity across development, testing, and production stages.
A scalable MLOps platform was required to standardize workflows, automate deployment pipelines, and accelerate the transition from experimentation to production.
Implementing an MLOps Platform for WGU
Addressing these challenges required more than deployment automation alone. A platform capable of supporting the full ML lifecycle, from experimentation to production, while maintaining flexibility, governance, and collaboration was essential.
Our team engaged as a strategic engineering partner to design and implement a scalable enterprise MLOps platform tailored to the client’s AI ecosystem.
1. Developing a Foundational MLOps Project Template
Addepto created a reusable MLOps template that serves as the foundation for all new AI and machine learning projects.
The template embeds best practices for model tracking, environment management, automated testing, and deployment workflows from the beginning of the development lifecycle, significantly reducing setup complexity and improving consistency across teams.
2. Integrating Notebook-Centric Development Workflows
The platform enables Jupyter and Databricks notebooks to function as integrated components of the production deployment pipeline.
By eliminating the need to rewrite notebook-based experimentation code for production use, the solution bridges the gap between Data Science flexibility and engineering-grade operational stability.
3. Separating Development, Testing, and Production Environments
Clearly defined development, staging, and production environments were implemented to isolate experimental workflows from live operational systems.
Environment separation enables Data Scientists, Analysts, and MLOps Engineers to collaborate more effectively while reducing deployment risks and preserving model integrity across the ML lifecycle.
4. Enabling Configuration-Driven Workflow Customization
The platform uses external configuration-driven orchestration to allow teams to customize workflows, validation logic, and deployment behavior without modifying the core codebase.
A configurable architecture improves flexibility while enabling standardized governance and reusable operational patterns across multiple AI projects.
5. Automating Deployment and Operational Workflows
Automated deployment pipelines and predefined validation logic were implemented to streamline repetitive operational tasks and accelerate model deployment cycles.
Automation reduced manual intervention, minimized deployment errors, and enabled faster transitions from concept validation to full-scale production.
“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 – Western Governors University
Accelerating AI Adoption Through Standardized MLOps Workflows
Addepto’s solution enabled WGU to streamline ML deployment workflows and reduce inconsistencies between experimental and production environments. By introducing a self-service MLOps platform, our team accelerated the transition from concept validation to production deployment, allowing the client’s Data Science team to focus more on developing advanced AI models ready for real-world use.
Beyond improving delivery speed, the platform established a standardized workflow with clearly separated development, testing, and production stages. This structured environment enabled Data Scientists, Data Analysts, and MLOps Engineers to collaborate and refine models independently without compromising model stability, governance, or deployment integrity.
Before
- Fragmented notebook and production environments
- Manual code refactoring before deployment
- Inconsistent ML deployment workflows
- Slow transitions from experimentation to production
After
- Standardized MLOps platform
- Seamless deployment from notebooks to production
- Automated and configurable deployment workflows
- Faster and more reliable AI operationalization
A scalable MLOps foundation positions WGU to continue expanding enterprise AI initiatives with greater speed, reliability, and operational efficiency.
As part of KMS Technology, Addepto continues to help organizations operationalize AI through scalable MLOps platforms and automation-first engineering practices.
Ready to accelerate AI deployment with MLOps? Contact us today!