Kavitha M Email & Phone Number

Mechanical Engineer from NIT Warangal (formerly REC Warangal) with 14+ years of experience in ML• Engineering/Data Science and CAE and Analysis.• Advanced proficiency and strong hands-on experience with:• Python, SQL, ML frameworks, libraries, data structures, Probability and Statistics, and AI architectures• Full-stack Machine learning Development from Data Engineering to ML Deployment using AWS• Databricks and PySpark.• In-depth knowledge of ML algorithms and its application• Experience with computer vision and image processing methods using Google Cloud Service.• Experience working in multi-cloud vendors for ML engineering and ML OperationsSkillsMachine Learning , Data Collection & Preparation, ML model development, Data Processing, Feature Engineering, EDA, Algorithm Research & Development, Model Evaluation & ValidationModel Deployment & Integration, Artificial Intelligence, Python, R, PySpark, MySQL, Flask, FastAPI, Docker, NLP, ML Ops, Github, Gitpod.Data Science: Linear Algebra, Discrete Mathematics, Web Scraping, Data Mining, Data Structures, Statistics, EDA and Data Pre-processing, Artificial Intelligence, Machine Learning, Deep Learning, NLP.ML Algorithms:Linear Regression, Logistic Regression, SVM, Decision Tree, Random Forest, KNN, PCA techniques, ensemble techniques, XG Boost, Cross Validation techniques, Hyper Parametric tuning techniques, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction, Natural Language Processing (incl. Theme deduction, sentiment analysis, Topic Modelling, Plagiarism, Cosine Similarity), Natural Language Generation, OCR, Stop words, Lemmatization, Vectorization, Keras, TensorflowIOT :Raspberry pi(4), Arduino, Wi-Fi/Bluetooth connection, Autonomous driving car model, ODB port data extraction, Remote control carCloud :Amazon Sage Maker, AWS S3, AWSAzure Databricks, Github, GitpodGoogle Cloud Vision Api (Vision AI)Job Summary Transforming data science prototypes and applying appropriate ML algorithms and tools.Enhancing the end-to-end AI/ML project lifecycle, from data collection and model preparation to deployment and maintenanceManage, maintain, scale, and improve machine learning models already running in production environments.Work with common ML algorithms and relevant software libraries.Deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.Optimize and tweak ML models according to how they behave in production

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  National Institute Of Technology Warangal

  Mechanical Engineering