Vinish Tomar

1. Developed and deployed predictive models using Python, TensorFlow, PyTorch, and scikit-learn, improving business performance by 25%. 2. Collaborated with cross-functional teams to integrate AI-driven solutions into enterprise applications, enhancing operational efficiency by 20%. 3. Designed and implemented end-to-end ML pipelines including data preprocessing, feature engineering, training, and real-time inference, ensuring reliability and scalability. 4. Utilized MLflow for MLOps, enabling experiment tracking, model versioning, and continuous performance monitoring. 5. Managed large datasets using SQL and PySpark, achieving a 30% reduction in data processing time through optimized distributed processing. 6. Deployed production-ready models on AWS, Azure, and Google Cloud (GCP), improving model accessibility and scalability across environments. 7. Created interactive dashboards and visualizations using Matplotlib, Seaborn, and Plotly, providing actionable insights to stakeholders and improving data- driven decision-making.