vybhav Ampabattuni

Master's

Computer Science

Alice consulting

Machine learning engineer

Implemented a Convolutional Neural Network (CNN) for image classification tasks and utilized MongoDB for efficient storage and retrieval of image data with achieving a validation accuracy of 87% on a benchmark dataset.  Collaborated with a team to fine-tune Large Language Models (LLMs) such as GPT-3.5 for NLP tasks, surpassing industry benchmarks and achieving remarkable performance improvements,enhancing language understanding and generating contextually relevant, human-like text for applications spanning chatbots, sentiment analysis, and content generation.  Deployed Git and GitHub for efficient version control, enabling seamless collaboration, streamlined code management, and effective branching and merging strategies.  Created a Python REST Microservice application with FastAPI and deployed it on Heroku, enhancing service accessibility and responsiveness for real-time data processing.  Developed a Natural Language to SQL project using OpenAI's text-davinci-002 API, enabling seamless conversion of human language queries into SQL queries, demonstrating strong proficiency in NLP and database integration.  Leveraged AWS SageMaker to accelerate end-to-end machine learning workflow, achieving 40% faster time-to-market and seamless scalability for production workloads.  Built a cutting-edge text generation language model using TensorFlow and NLP, achieving an impressive 92% coherence rate in generating contextually relevant text from prompts.