Elevating Business Impact with Advanced Analytics and MLOps

A leading quick-service restaurant chain faced a common challenge: operationalizing machine learning (ML) models efficiently in a legacy environment. Elder Research, in partnership with other teams, used machine learning operations techniques to optimize this client’s business operations across 13 major objectives in 15 months, saving more than 1,200 hours of processing time.

Our team streamlined processes, reduced costs, and enhanced decision-making by leveraging cutting-edge tools like AWS, Apache Airflow, and Azure Databricks to build and maintain advanced ML pipelines. The client achieved significant improvements in demand and sales forecasting, anomaly detection, staff programs, and process optimizations.

Like many organizations, our client aimed to operationalize machine learning models efficiently in their legacy environment. But outdated infrastructure and siloed processes hindered scalability and performance. Data scientists often ran models locally, leading to inconsistencies between development and production environments, manual handoffs, and prolonged deployment cycles. This not only slowed down innovation and production readiness but also introduced risks in model performance and reliability after deployment.

Our client asked us to implement and improve a robust MLOps infrastructure that would bridge the gap between data science, ML model development, and DevOps. The goal was to enable seamless model management, deployment, and monitoring.

The right solution required streamlined processes that support end-to-end ML workflows, including automated testing, model versioning, and integration into production pipelines. By shifting from fragmented, ad-hoc deployments to a cohesive MLOps framework, companies like our client could reduce operational costs, improve model reliability, and empower cross-functional teams to collaborate effectively, driving better business outcomes through enhanced forecasting, anomaly detection, and overall operational efficiency.

To achieve this goal, Elder Research meticulously planned the building and migration of ML pipelines using Miro diagrams to map workflows, breaking them into smaller, manageable tasks with defined dependencies.

These tasks were tracked in Jira and executed through monthly sprints, ensuring a systematic and agile approach to implementing a comprehensive MLOps framework.

The framework allowed us to successfully deploy end-to-end ML processes on Azure Databricks and AWS Managed Airflow platforms. Azure Databricks was particularly favored over Apache Airflow for some of the ML pipelines because of the platform’s Spark-based parallel computing, which enabled faster processing and reduced costs.

We used the tools in the client’s existing environment to implement the MLOps framework. Where tools were lacking, our team recommended cost-effective options such as free open-source tools.

Collaborating closely with the ML infrastructure platform team, Elder Research integrated Amazon CloudWatch for logging and monitoring and Docker for containerization, ensuring consistent environments across development and production.

Amazon S3 was used for data storage and Redshift for optimized data warehousing. To improve robustness and maintainability, the team incorporated logging with Amazon CloudWatch and Databricks’ logging functionality, implemented error handling, and used Docker to provide consistent deployment environments.

MLFlow facilitated model versioning and lifecycle management, enhancing reproducibility.

Continuous integration and deployment (CI/CD) were established using GitHub Actions, automating the deployment and updates to production environments.

Data validations were conducted with Great Expectations, ensuring data integrity at every pipeline stage.

Our team collaborated closely with our data scientists and the client’s in-house data scientists to perform QA testing, ensuring consistent outputs and addressing discrepancies, which included fixing issues related to asynchronous data refreshes and ensuring model reproducibility through version control. Comprehensive documentation and comments were added to facilitate troubleshooting and long-term maintainability.

Elder Research’s targeted ML engineering support achieved significant operational improvements by deploying and migrating six ML models across AWS Managed Airflow and Azure Databricks platforms. By building, migrating, and maintaining 13 production ML pipelines, the team streamlined demand forecasting, fraud detection, and sales forecasting processes, resulting in enhanced model performance, accuracy, and reliability.

These efforts collectively saved more than 1,200 hours of processing time, reducing operational costs and freeing resources for other initiatives. Enhanced logging, error handling, and robust documentation improved pipeline maintainability and facilitated seamless collaboration among the data engineering, data science, and ML engineering teams.

The use of advanced tools such as Amazon S3, Redshift, CloudWatch, MLFlow, Docker, and GitHub for CI/CD ensured a consistent and scalable MLOps infrastructure. The implementation of data validation through Great Expectations further guaranteed data quality, empowering the client with reliable, actionable insights that drive enhanced stakeholder value and operational efficiency.