Led MLOps enablement for ML and statistical models on Azure Databricks, optimizing deployment workflows and model governance.

Architected the end-to-end ML lifecycle using Azure-native services, including data ingestion, preprocessing, model training, validation, deployment, and monitoring.

Built CI/CD pipelines in Azure DevOps for automated testing, packaging, and deployment of models and data pipelines, improving deployment frequency and reducing manual intervention.

Developed a machine learning model to predict the segment of leads with predictive scores.

Designed and implemented end-to-end deployment workflows for models using Docker,AWS-ECR,ECS,SNS and Step Functions ensuring seamless scalability and reliability.

Developed models to classify sentiments and provided actionable recommendations to enhance brand strategy.

Image segmentation and image classification generated by sensors.

Image data cleaning

Troubleshooting of machine learning models.

Understanding why clients are most likely to leave can lead to actions to improve client's retention.

Finding the patterns of clients trading behavior with different techniques.

RFM model, clustering and presents the insights to management.

Develop a machine learning model for risk analytics in banking and financial services.

Utilize data in minimizing the risk of financial loss while lending to customers.

Developed an image classifier model to accurately identify faulty photovoltaic modules, improving maintenance and operational efficiency.

Worked on data analysis of machine equipment parts to derive business insights.

Facilitating better decision-making for revenue growth.