Model Enhancements with Automated Incremental Training Pipeline For An USA Business

Real Results Happen With GetOnData

70 %

Improvement in model performance

25 %

Efficient utilization of resources

30 %

Scalability to handle large volumes of data


The objective was to develop an advanced incremental training pipeline customized for our e-commerce client, aimed at boosting model performance and agility. By using MLFlow, DVC, Git, and FastAPI, our goal was to streamline model updates every 4 hours, ensuring the system stays responsive to changing data trends.

The Solutions

MLFlow Integration

MLFlow Integration

Using MLFlow for experiment tracking, model versioning, and registry management ensured organized and transparent workflow management.

DVC Implementation

Utilizing DVC for data version control and pipeline orchestration facilitated efficient management of large datasets and tracking dependencies between data, code, and models.

Git for Version Control

Git for Version Control

Employing Git for version control of the codebase enabled collaborative development and ensured tracking of changes made to the code.

FastAPI For Deployment Automation

FastAPI for Deployment Automation

Implementing FastAPI to create a web-based API endpoint streamlined deployment processes, enabling rapid development of web APIs with automatic documentation generation.

Data Flow

Business Impact

Improved Model Performance

Regular updates and retraining based on new data led to a 70% improvement in model performance, resulting in better predictions and more reliable insights for the business.

Scalability and Resilience

The automated training pipeline proved resilience and scalability while dealing with enormous amounts of data and complex modeling jobs.

Efficient Resource Utilization

Automating the training pipeline freed up valuable human resources, allowing data scientists and engineers to focus on strategic activities, contributing to a 25% increase in productivity.

Consistency and Reproducibility

Using version control systems such as Git and DVC ensured model consistency and reproducibility throughout multiple iterations, resulting in a 30% reduction in new feature development time.

Client’s Quote

We are profoundly grateful for our collaboration with GetOnData, as it has changed our machine learning model development methodology. The implementation of an automated incremental training pipeline has not only elevated our model performance to unprecedented levels but also empowered us to allocate our resources with remarkable efficiency. This technical innovation has been instrumental in driving superior business outcomes, marking a milestone in our journey towards excellence.

Case Studies

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