No-Code ML Platform
Enterprise-grade no-code ML platform with time series forecasting module, reducing baseline model development time by 80% for forecasting workflows.
Overview
Developed a comprehensive time series forecasting module for an enterprise no-code ML platform, enabling non-technical users to build sophisticated machine learning models without coding expertise.
Key Achievements
- 80% Reduction in Development Time - Automated 7+ manual steps including data preprocessing and feature engineering
- Enterprise Deployment - Solution deployed across major organizations including Kyndryl and Xyzies
- 99.9% Platform Uptime - Implemented MLOps best practices ensuring high reliability
Features
- Automated Data Preprocessing - Handles missing values, outliers, and data normalization
- Feature Engineering - Automatic feature extraction for time series data
- Model Versioning - Track and manage multiple model versions
- Automated Testing - Continuous integration for model validation
- Monitoring & Observability - Real-time metrics with Grafana dashboards
Tech Stack
Data Processing
- Pandas for data manipulation
- PySpark for large-scale data processing
ML Frameworks
- TensorFlow for deep learning models
- Scikit-learn for classical ML algorithms
Infrastructure
- Docker for containerization
- K9S for Kubernetes management
- Grafana for monitoring and visualization
MLOps Practices
- Model versioning and artifact tracking
- Automated testing pipelines
- Continuous integration and deployment
- Performance monitoring and alerting