Krishna Kankipati
A Bit About Me
🤖🌍 Introducing your friendly neighborhood Machine Learning Enthusiast and AI Wizard! Fueled by a lifelong fascination with robots and artificial intelligence, I've embarked on a thrilling journey to explore the magical realms of machine learning.
🔮 With my enchanting arsenal of skills in Statistical Data Analysis, TensorFlow, Machine Learning, and Deep Learning, I'm on a mission to cast spells of ML applications onto real-world problems and bewitching computer vision projects. Together, let's create a world where technology and humanity harmoniously coexist.
🧙♂️ My adventures include a mystical internship as an AI & Web Engineer, where I conjured up a multilingual chatbot to charm hungry patrons and boost restaurant businesses. As an AI Research Apprentice, I crafted an LSTM model using the enchanted TensorFlow framework, foreseeing adverse digressions in critical industrial processes with a remarkable 90% accuracy.
Projects
GitHub - Krishna2709
SQL Injection Payload Generator
Generative AI Applications
Automatic Tweets Extraction and Analysis (MLOps)
Titanic Passengers Survival Prediction (MLOps)
NY Taxi data analysis and Taxi Fare Prediction (Data Engineering)
Classification of Rice crops from Sentinel-1 and Sentinel-2 data (MLOps)
• LLM application built on LangChain and powered by fine-tuned open-source LLM models to generate SQL Injection Payloads.
(fed up with ChatGPT limitations and restrictions)
• Q&A Generating Agent (OpenAI, Streamlit, GCP) - Q&A Agent
• Improved tweets extraction time by implementing an automated pipeline using Prefect (Flows, Deployments & Schedules), and TensorFlow for categorization, and created an analytical dashboard using D3.js and Streamlit. Leveraged AWS (Sagemaker, Lambda, EventBridge & S3) to shift the code package into the cloud.
• Developed a production-ready model package (ongoing) using Feature Engine, Scikit-Learn, Tox, Pydantic, PyTest, and FastAPI for model serving. Continuous testing and input validation improved model robustness and reliability. Achieved an accuracy of 85% on the test data.
• Built an end-to-end ETL pipeline using PSQL, Docker, Terraform, BigQuery, dbt, and Spark to analyze and predict taxi fares. Created a robust Machine Learning algorithm using hyperparameter tuning and preprocessing techniques. Achieved 98% accuracy in fare prediction and reduced data processing time by 70%.
• Developed a production-ready code package (ongoing) using Feature Engine, Scikit-Learn, Pydantic, PyTest, and FastAPI for model serving. Used TensorFlow’s high-level API for efficient model development. Achieved an accuracy of 90% on the test data and reduced the model training time by 40% using GPU acceleration.
Work Experience
Sept 2022 - December 2022
Sept 2022 - December 2022
May 2020 - August 2021
August 2020 - October 2020
July 2020 - October 2020
February 2020 - March 2020
January 2020 - March 2020
AI & Web Engineer Intern - NolymitAI
Designed and implemented a multilingual chatbot tailored explicitly for the restaurant industry. This AI-driven solution resulted in a significant 35% increase in orders, decreased customer service requests by 25%, and improved overall customer satisfaction.
Developed a comprehensive web application platform designed for small-scale businesses. By integrating machine learning applications into the forum, I contributed to a 30% boost in sales. I also incorporated Firebase, Node.js, and Explicit Content Detection features, leading to a 20% reduction in inappropriate content and enhancing customer retention.
Enhanced project management efficiency by coordinating weekly meetings, preparing detailed minutes, generating strategic work plans, and providing comprehensive work reports. These efforts led to a 30% reduction in project completion time and increased team productivity by 20%.
Graduate Student Assistant - Stevens Institute of Technology
As a Graduate Student Course Assistant for the graduate-level Algorithms course, I had the privilege of aiding 50+ students in their academic journey. My responsibilities included facilitating a better understanding of complex algorithms concepts, coordinating meetings with the professor when needed, and offering personalized guidance to students to enhance their learning experience.
Additionally, I contributed to the development of course materials. This involved designing and evaluating assignments and midterm examination papers, ensuring they comprehensively assessed students' understanding of the course content.
Throughout this role, I honed key communication, student engagement, and development skills. In addition, this experience underscored the importance of adapting to individual learning styles and fostering an environment conducive to student success.
Jr Software Developer - Image Classification and Object Detection
Vayu Drone Technologies Pvt Ltd
Designed advanced machine learning models to accurately differentiate between cultivated and uncultivated land, achieving a 30% reduction in image processing time and enhancing agricultural data accuracy for clients by 20%.
Pioneered the development of robust object detection algorithms for efficient urban planning, reducing processing time by 20%, and successfully categorizing over 1,000 parking spots and 500 miles of roadways to streamline city infrastructure planning.
Spearheaded the creation of a machine learning model for barren land detection, processing over 500 high-resolution drone images daily, leading to the precise classification of more than 10,000 square miles of land.
AI Research Intern - Widhya
Designed and implemented an LSTM model using the TensorFlow framework to predict adverse digressions in a steel manufacturing company's critical and industrial noise process with 90% accuracy, resulting in a 75% reduction in
unplanned downtime and a 60% decrease in maintenance costs and a 50% increase in production throughput.
Observed more than 95% change in system entropy when the machine approached a failure and formulated a
method to calculate the confidence score. This highly improved the reliability of the model's prediction.
By predicting failures early, reduced time delay in repairing and replacing machines, resulting in a 20% increase in production capacity, and a 50% reduction in equipment replacement costs.
Machine Learning Intern - Robofied
Produced a series of highly informative and comprehensive technical articles on Machine Learning and Deep Learning, and referencing research papers and technical reports to gain extensive theoretical knowledge and insight
Demonstrated technical proficiency by implementing the respective work with the Scikit-learn and TensorFlow framework, resulting in a 30% increase in website traffic and a 15% increase in engagement with the articles.
Machine Learning Intern - Cognia Technologies
Leveraged XGBoost Regression algorithm for predicting taxi demand in Hyderabad city.
Achieved a 90% explained variance score, which helped the client to monitor the taxi allocation effectively.
Machine Learning Intern - Exposys Data Labs
Reproduced a research paper on detecting DDoS attacks using a multi-classifier approach. Achieved 85% accuracy in detecting a DDoS attack.
Built an offline data model by ensembling different classification algorithms Logistic Regression, Random Forest, XGBoost, and Neural Network and used the majority voting method to finalize the prediction.
Optimized the model by creating a pipeline for data preprocessing, feature engineering, and model predictions.