Client: WGU

Case Study: Implementing an MLOps Platform for Seamless Transition from Concept to Deployment

Case study details

The client, an educational institution, is renowned for its commitment to providing flexible, affordable, and accessible online education in fields such as IT, teaching, business, and healthcare. The university has recognized the necessity of integrating MLOps into its operations to streamline and automate the lifecycle of machine learning models, which includes model development, deployment, and monitoring.


The company, recognizing the importance of AI for enhancing business performance, selected the Addepto Data Engineering team to develop an experimental platform. This platform is designed to facilitate the transfer of their internally created models into a production-ready environment.


Addepto’s strategy was to use Databricks notebooks to foster collaboration between Data Scientists and Engineers. Addepto created a platform that acts as a foundational template incorporating MLOps best practices, automating the setup and update processes, and allowing for seamless workflow adaptation across different environments.


The project aimed to seamlessly integrate Data Science experimentation with MLOps practices, ensuring AI models are production-ready without code rewriting or testing. It was about to merge the experimental agility of Data Science with the operational rigor of MLOps, enhancing the deployment process’s efficiency and reliability.


The Addepto platform streamlined the deployment process, minimized development and production environment discrepancies, and expedited the transition from concept to full-scale production. This approach not only accelerated the workflow but also allowed the Client’s Data Science Team to focus on developing sophisticated AI models for immediate practical application.

Our team expert opinion


The platform designed by Addepto acts as a 'foundational template' for launching new projects

Databricks partially addresses the issue by recognizing the preference for Notebooks during the experimentation phase. It offers a unified platform that allows Data Scientists to share their Notebooks with Data Engineers and other collaborators.

However, the Addepto Team aimed to advance this integration further. We enabled Data Scientists to use notebooks as integral parts of workflows that seamlessly adapt across development, staging, and production environments.

During project development, our team worked on:

  • The platform designed by Addepto acts as a 'foundational template' for launching new projects. It incorporates 'built-in' MLOps best practices, ensuring a standardized approach to project setup. This design automates various setup and update processes, minimizes manual intervention, and offers essential features for integrating Data Scientists' Notebooks with project configurations.
  • Addepto designed a flexible workflow that accommodates project-specific needs while integrating MLOps' established stages—such as preliminary data processing, automatic verification, and testing—through easily modifiable configuration files. This standardized workflow, with its separated and well-defined stages, allows for the isolation of experimental, testing, and production environments. Consequently, Data Scientists, Data Analysts, and MLOps Engineers can continuously enhance the model at any stage without compromising its integrity.

Let our clients do the talking

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


Empowering Seamless Deployment and Workflow Integration with MLOps Platform

Addepto’s platform empowered the Client to streamline the deployment process and minimize the discrepancies between development and production environments. By providing a self-service platform, it facilitated a quicker transition from concept to full-scale production, allowing the Client’s in-house Data Science Team to concentrate on developing sophisticated AI models ready for immediate practical use.

Yet, acceleration wasn’t the only benefit of this project. By integrating a standardized workflow with distinct and well-defined stages, Addepto successfully segregated the experimental, testing, and production environments. This separation enabled Data Scientists, Data Analysts, and MLOps Engineers to refine models at any stage without compromising their integrity.


  • Significant challenges in transitioning models from the experimental phase to production due to the separation of environments.
  • Necessity for code refactoring in order to apply MLOps best practices MLOps Engineers, introducing risks of errors and inconsistencies.
  • Slowed deployment process, hindering efficiency and reliability of model deployment and scaling.


  • The smoother and more efficient transition of models from concept to deployment with integrated workflows.
  • Seamless adaptation across development, staging, and production environments facilitated by automation.
  • Expedited transition to full-scale production

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