Achieving Remarkable Model Enhancements Through Automated Incremental Training Pipeline for US client

Success Metrics: Real Results Happen With GetOnData

70%

Improvement in model performance

25%

Efficient utilization of resources

30%

Scalability to handle large volumes of data

Objective

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.

Achieving Remarkable Model Enhancements Through Automated Incremental Training Pipeline for US client

The Challenges

  • Model Staleness: Existing predictive models lacked relevance as they couldn't integrate the most recent data, resulting in inaccurate predictions that hampered decision-making.
  • Manual Processes: Data scientists spent a lot of time manually retraining models, limiting their ability for practical strategic analysis.
  • Lack of Scalability: As the data amount and complexity expanded, traditional model training procedures struggled to scale successfully, creating problems for our operations and growth.
  • Model Staleness: Existing predictive models lacked relevance as they couldn't integrate the most recent data, resulting in inaccurate predictions that hampered decision-making.
  • Manual Processes: Data scientists spent a lot of time manually retraining models, limiting their ability for practical strategic analysis.
  • Lack of Scalability: As the data amount and complexity expanded, traditional model training procedures struggled to scale successfully, creating problems for our operations and growth.

The Solutions

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

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

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.

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

Data Flow

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.