Projects
Vasuki - ChatMate - NLP/NLU (Natural Language Processing/Natural Language Understanding)
Vasuki is designed to be an intelligent chatbot that can interact with users in a meaningful and context-aware manner. It uses advanced NLP techniques to understand and respond to user queries, providing a seamless conversational experience.
https://vasuki-chatmate.streamlit.app/
.. finetuning still in progress ..
.. development in progress ..
The development of Vasuki has been a multifaceted journey involving various advanced techniques in Natural Language Processing (NLP), data management, and user interface design.
Overview:
1. NLP Techniques and Foundations
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Learning Paradigms:
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Implementing zero-shot, one-shot, and few-shot learning to enable the chatbot to generalize from minimal examples.
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Text Summarization:
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Developing models to condense lengthy dialogues into concise summaries, aiding coherent response generation.
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2. Utilizing Pre-Trained Language Models
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Transformer Models:
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Leveraging advanced models for summarization, translation, and question answering.
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Model Fine-Tuning:
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Customizing pre-trained models on specific datasets to enhance targeted application performance.
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3. Building Vasuki – A Proof of Concept
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Dialog Summarization:
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Implementing techniques to process and condense dialogue inputs for better context retention and response generation.
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Inference Techniques:
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Using one-shot and few-shot learning strategies to generate accurate responses with minimal training examples.
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Retrieval-Augmented Generation (RAG):
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Combining retrieval mechanisms with generative models to create a robust and knowledgeable conversational agent.
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4. User Interface Development
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TruthSeeker Experience:
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Drawing from the TruthSeeker project to design an intuitive and interactive UI for Vasuki.
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5. Comparative Analysis and Evaluation
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Benchmarking:
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Comparing Vasuki’s performance with established conversational agents like ChatGPT and Gemini to identify strengths and areas for improvement, acknowledging the differences in scope and resources.
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6. Deployment and Accessibility
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Cloud Deployment:
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Ensuring scalability and accessibility by deploying the chatbot on cloud platforms.
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Current Phase: Fine-Tuning and Development
The project is currently in the fine-tuning and development phase, including the creation of knowledge graphs and metadata.
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Knowledge Graphs:
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Integrating knowledge graphs to enhance Vasuki's contextual understanding and reasoning capabilities.
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Metadata Creation:
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Annotating data with relevant metadata to improve data retrieval and organization.
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Additional Components:
Workflow Management and Model Pipelines
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Directed Acyclic Graphs (DAG):
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Using DAGs for efficient workflow management, ensuring that each task is executed in the correct sequence.
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Constructing model pipelines to handle data preprocessing, feature extraction, model training, and evaluation in a structured manner.
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Knowledge Representation
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Undirected Acyclic Graphs (UAG):
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Utilizing UAGs for hierarchical knowledge representation, ensuring no redundant paths and a clear structure.
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Database Design and Optimization
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Cardinality:
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Designing database schemas based on cardinality to manage user data, queries, and session information efficiently.
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Optimizing queries by understanding the relationships and unique values within the data.
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User Experience:
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Seamless Interaction:
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Focusing on delivering a seamless and engaging user interface that draws users in and provides intuitive interactions.
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Public Interface:
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The chatbot user interface is now live, providing users with a firsthand experience of Vasuki’s capabilities.
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.. Get ready to experience the future of AI conversations with Vasuki—your new intelligent ChatMate!"
1. TruthSeeker - NLP (Natural Language Processing) Project
Model that leverages the power of Artificial Intelligence (AI) and Machine Learning (ML) to separate facts from fiction.
Powered by NLP wizadry, it classifies news articles either as propogandistic or non-propagandistic, so you can navigate the media maze with ease.
In the TruthSeeker project, I single-handedly managed and executed all aspects of the project, showcasing a comprehensive skill set and high level of autonomy. My contributions included:
Machine Learning Development: I was solely responsible for developing and fine-tuning advanced machine learning models. This process involved selecting the right algorithms, optimizing them for peak performance, and validating their accuracy and reliability.
Feature Engineering: I personally undertook the feature engineering process, utilizing various techniques like Bag of Words, TF-IDF, Word2Vec, ELMO, NMF, and BERTopic, which was instrumental in refining the data for effective model training.
Diverse Algorithm Implementation: I implemented a broad spectrum of algorithms for classification and deep learning tasks, including Logistic regression, Naive Bayes, Light GBM, XG Boost, CNN, RNN, LSTM, Bidirectional LSTM, GRU, and transformer models such as BERT, RoBERTa & XL-Net. Handling this diversity single-handedly highlights my versatility and deep understanding of machine learning.
Cloud Deployment and Web Application Development: I personally deployed these machine learning models to the cloud and developed a user-friendly front-end application using Streamlit. This not only made the models accessible for practical use but also showcased my skills in cloud technology and user interface design.
Project Management and Leadership: Despite working alone, I demonstrated project management and leadership skills by planning, coordinating all project phases, meeting deadlines, and maintaining high-quality standards throughout the project lifecycle.
This experience was a testament to my ability to handle a project end-to-end, from the technical development of machine learning models to their deployment and user interface design, all while ensuring the project aligned with its goals and timelines.
https://truthseeker-an-app-that-separates-facts-from-fiction.streamlit.app/
2.GeocabMatrix_RidePredictorX
TimeSeries Forecasting Project
An advanced predictive model designed to forecast the number of taxi rides for any given location at specific times of the day for NYC.
The model utilizes sophisticated data analysis techniques to accurately anticipate ride demand, enabling taxi companies to strategically allocate their resources.
The implementation of this model significantly enhances the operational efficiency of taxi companies. It ensures a more dynamic and responsive service by predicting and meeting the fluctuating demands for rides. This not only improves customer satisfaction by reducing wait times but also optimizes the fleet usage, leading to increased profitability and resource management efficiency.
Data Science Expertise: Spearheaded the analysis and processing of a complex, unstructured dataset comprising 18 million records. Demonstrated exceptional expertise in defining the structure of the dataset and extracting meaningful insights, which involved a deep understanding of data science principles and practices beyond standard norms.
Machine Learning Proficiency: Applied advanced machine learning techniques to forecast taxi ride demand, emphasizing my robust skill set in this domain. This included not only implementing existing algorithms but also customizing and fine-tuning them to handle the complexities and unstructured nature of the data efficiently.
Data Processing and Feature Engineering: Dedicated a substantial portion of my role to process the raw data into a structured and usable format. Engineered 648 distinctive features, showcasing an advanced level of proficiency in transforming raw data into valuable inputs for machine learning models.
Feature Store Utilization: Implemented the use of HopsWorks Feature Store for efficient management and storage of features. This ensured scalability and ease of access, significantly enhancing the modeling process. Employing the MLOps approach, the project adopts the FTI (Feature, Training, Inference) architecture. Implemented Features/Training/Inference Pipelines.
This architecture provided a cohesive framework that encompasses both batch and real-time ML systems, organizing operations into three distinct pipelines. A crucial aspect of the development journey involves prototyping and experimentation. Google Colab | Jupyter notebooks | Visual Studio served as the cornerstone for these activities within this project.
Cloud Computing and Application Development: Deployed the predictive model on the cloud and developed an intuitive web application using Streamlit. This aspect of my role highlights my capability in making data science solutions operational and user-friendly, catering to real-world applications.
In summary, my role in this project underscores my high-level responsibility and skill in managing, analyzing, and deriving actionable insights from large-scale, complex data sets. It also demonstrates my expertise in developing and deploying machine learning models in a user-centric, real-world application. This project is a testament to my comprehensive abilities as a Senior Data Scientist and Machine Learning Engineer.
3. HealMetrics - HealthCare Analytics Project
A healthcare analytics project focused on leveraging cutting-edge technology to predict hospital
length of stay (LOS) for patients. Utilizing Snowflake for data management and AWS SageMaker for machine
learning, the project aims to improve patient care and optimize hospital resource utilization.
Responsibilities:
ETL Processes: Conducted Extract, Transform, Load (ETL) processes in Snowflake, using SQL queries and Common Table Expressions (CTEs) to handle raw healthcare data efficiently.
Data Preprocessing: Utilized AWS SageMaker for data preprocessing, ensuring that the data was clean, formatted correctly, and ready for further analysis.
Feature Engineering: Employed feature engineering techniques in AWS SageMaker to create new features from the raw data, enhancing the predictive power of the machine learning models.
Model Development: Developed machine learning models in AWS SageMaker, leveraging various algorithms to analyze healthcare data and extract meaningful insights.
Model Evaluation: Implemented a process to insert model predictions back into Snowflake, allowing for the evaluation of model accuracy against a predefined threshold of 85%.
Real-time Monitoring: Established an automated email notification system triggered by a scoring script, which alerts stakeholders in real-time if the model's accuracy falls below the set threshold, ensuring continuous model performance monitoring.
Cloud-based Solution: Designed and implemented a cloud-based solution using Snowflake and AWS SageMaker, showcasing proficiency in leveraging cloud tools for data analytics projects.
Project Management: Managed the project from conception to completion, ensuring that all milestones were met and deliverables were of high quality.
Stakeholder Communication: Maintained regular communication with stakeholders to ensure alignment with project goals and to address any concerns or feedback.
Documentation: Documented all processes, methodologies, and outcomes of the project, ensuring that the knowledge gained from the project was captured and shared effectively.
Key Achievements:
Achieved high accuracy in predicting patient LOS, directly contributing to enhanced patient care planning and resource allocation.
Demonstrated the effective use of Snowflake and AWS SageMaker in a healthcare analytics context, showcasing the ability to handle large datasets and deploy scalable machine learning solutions.
Established a robust framework for real-time data analysis and prediction, enabling the hospital to make informed decisions and improve operational efficiency.
