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case-study-mlops

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Client

Confidential

Catagories

AI, Machine Learning, ML OPS

Industry

Ecommerce

Download Case Study

End-to-End MLOps with CI/CD

 

Problem Statement‬

A leading e-commerce company approached us with a requirement to build a robust MLOps framework for automating the end-to-end ML lifecycle. The system needed to:

  1. Automate data ingestion, preprocessing, and feature engineering to enhance model performance.
  2. Ensure model versioning and reproducibility across multiple training iterations.
  3. Deploy ML models efficiently using a continuous integration and deployment pipeline.
  4. Monitor model drift and performance metrics to trigger automatic retraining when necessary.
  5. Scale to support high-volume transaction data in real time while ensuring minimal downtime.


Challenges

  1. Model Versioning and Reproducibility: Ensuring consistency across multiple training iterations.
  2. Automated Training and Deployment: Eliminating manual steps in ML model deployment.
  3. Scalability: Supporting high-volume transaction data in real time.
  4. Performance Monitoring: Tracking model drift and automating retraining when necessary.
  5. CI/CD Integration: Creating an efficient workflow for continuous model updates.

Solution Design Delivery

 

Phase 1: ML Model Development

Developed a scalable ML pipeline by automating data processing, training, and experiment tracking.

  1. Data Pipeline Implementation: Collected and preprocessed customer transaction data and automated feature engineering using Spark and Pandas for scalability.
  2. Model Training and Optimization: Developed baseline models using XGBoost and deep learning approaches and Applied hyperparameter tuning with Optuna for optimal performance.
  3. Experiment Tracking: Used MLFlow for tracking experiments and managing model versions.

 

Phase 2: CI/CD Pipeline for MLOps

Automated model deployment and monitoring using containerized workflows and real-time tracking.

  1. Continuous Integration (CI): Implemented automated testing with unit tests for data validation and model accuracy. Used GitHub Actions and Jenkins for CI workflows.
  2. Continuous Deployment (CD): Leveraged Docker and Kubernetes for containerized deployments. Integrated ML models with cloud services like AWS Sagemaker and GCP Vertex AI. Deployed models as RESTful APIs for real-time predictions.
  3. Monitoring and Automation: Established model monitoring using Prometheus and Grafana. Implemented automatic model retraining triggers based on performance metrics.
    Enabled model rollback in case of performance degradation.

Company overview

Client name: Confidential
Services: MlOps, ML, CICD
Technology: MLFlow, Airflow, PyTorch, Data Lake, Sagemaker
Industry: Ecommerce
Location: US

Details

The client is US leading global company that provides human resources (HR), payroll, and workforce management solutions.

How we Helped

Dedicated Team with cloud expertise

Budget Optimisation

On time Delivery

Our Approach And The Solution

Initial Implementation: Delivered an MLOps framework that automated the entire ML lifecycle. Reduced model deployment time by 60% through CI/CD automation. Improved model accuracy by 15% with continuous monitoring and retraining.

Post Optimization: Scaled to support multiple models and real-time inference with minimal latency. Integrated automated rollback mechanisms, achieving 99.8% uptime. Enhanced reproducibility with MLFlow-driven experiment tracking and version control.

  • Iterative Development: Starting with a baseline model enables optimization and scalability.
  • CI/CD Efficiency: Automating model deployment streamlined the ML lifecycle and improved reliability.
  • Scalability: Kubernetes and microservices architecture ensured seamless scaling for high-volume transactions.
  • Performance Monitoring: Real-time tracking and automated retraining enhanced model accuracy over time.
  • Data Security: Strong encryption and role-based access control ensured compliance with data privacy regulations.
  • Seamless Integration: Connecting with cloud services improved efficiency and deployment speed.
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