I am a Machine Learning and Web Engineer with a passion for creating intelligent, scalable solutions that transform data into actionable insights. With expertise in Computer Vision, AI, Data Analytics, and Cloud Deployments using Docker, AWS, and GCP, I specialize in both the development and deployment of data-driven applications that make an impact.
- 🌟 Skills: Python, JavaScript, TypeScript, C++, Data Science, ML & AI, Docker, Cloud
- 📈 Specialties: Computer Vision, Cloud-Based ML Deployments, Full-Stack Development
- 🎯 Current Focus: Building scalable ML solutions and deploying AI models with cloud infrastructure
I leverage Python and its extensive libraries to process and analyze complex data sets:
- Pandas: Efficient data manipulation and transformation for exploratory data analysis.
- NumPy: Numerical operations and array manipulations for large-scale data sets.
- Scikit-Learn: Building traditional machine learning models for predictive analytics.
With a focus on computer vision and AI, I work with powerful libraries and frameworks:
- TensorFlow & PyTorch: Developing and deploying neural network models for deep learning and AI solutions.
- OpenCV: Computer vision applications, including image processing, feature extraction, and object detection.
- Keras: High-level neural networks API for rapid prototyping and model tuning.
I build data-driven, responsive web applications and APIs using Python frameworks:
- Django: Crafting robust, full-featured web applications with integrated backend solutions.
- Flask: Developing lightweight, RESTful APIs and microservices to serve ML models.
- FastAPI: High-performance web frameworks for seamless API integration and deployment.
With a strong background in containerization and cloud services, I manage scalable deployments:
- Docker: Containerizing applications and ML models for efficient and reproducible deployments.
- AWS & GCP: Cloud solutions for end-to-end deployment pipelines, model hosting, and scalable infrastructure.
- Kubernetes (in progress): Exploring orchestration for managing containerized applications in production.
