I’m a passionate Data Scientist with a focus on leveraging advanced data science techniques such as machine learning, deep learning, and predictive analytics to solve complex problems across various domains.
Throughout my career, I have built and deployed scalable machine learning models, optimized data pipelines, and contributed to cloud-based architecture to ensure efficient data-driven solutions.
With expertise in Python, R, SQL, TensorFlow, Docker, and cloud platforms like AWS and Azure, I specialize in creating solutions that drive business decisions and innovation.
I am committed to transforming raw data into meaningful insights and helping businesses make data-driven decisions with clarity and precision.
Deep Learning-Based Chicken Disease Classification: The project uses deep learning techniques to preprocess image data and train a model to classify chicken diseases. It incorporates a pipeline for data preprocessing and model training with version control managed via DVC.
CI/CD Integration and Containerization: GitHub Actions automates continuous integration and deployment, using Docker for containerization. The deployment workflows ensure seamless building, testing, and running of the application in cloud environments.
Cloud Deployment on AWS and Azure: The project supports deployment on AWS EC2 and Azure Web App. AWS deployment involves utilizing ECR for Docker image storage and EC2 for container execution, while Azure deployment leverages the Azure Container Registry and Web App Server for hosting the application.
Exploratory Data Analysis (EDA): The project analyzes math score distributions across demographics, identifies correlations with socio-economic factors, and detects patterns and outliers to inform feature selection.
Data Transformation Pipeline: A preprocessing pipeline handles missing values, scales numerical data, and encodes categorical features using ColumnTrans former, ensuring high-quality and consistent input for machine learning.
Model Training and Deployment: Models are trained and optimized via Grid Search, evaluated with MAE and RMSE, and prepared for redeployment on AWS Elastic Beanstalk
Advanced PDF Information Retrieval with Semantic Understanding: Implemented a robust pipeline for extracting and preprocessing content from PDFs, utilizing Optical Character Recognition (OCR) for scanned documents and embedding-based methods to semantically structure data. This ensures accurate alignment with large language models (LLMs) for advanced query answering.
Contextual Compression for Optimal Data Ingestion: Designed and integrated contextual summarization techniques, leveraging transformer-based architectures to reduce irrelevant data and noise while retaining critical semantic context. This preprocessing step enhances the efficiency and relevance of inputs fed into LLMs.
Enhanced Query Response via Adaptive Data Preprocessing: Combined hierarchical embeddings with fine-tuned LLMs to dynamically extract and rank document segments based on user query intent, enabling precise, context-aware answers directly from large PDF datasets.
In-Depth Exploratory Data Analysis (EDA): Conducted thorough EDA to uncover data patterns and relationships, forming the foundation for effective feature engineering and model development.
Advanced Feature Engineering and Model Validation: Developed meaningful features and implemented a robust model with rigorous testing and validation, ensuring high predictive performance.
MLOps Integration with ZenML and MLflow: Utilized ZenML and MLflow for seamless experiment tracking and deployment, exemplifying proficiency in modern MLOps practices.
Custom Implementation of AlphaFold Architecture: Recreated the AlphaFold model using Python and PyTorch, focusing on the intricate neural network layers and operations essential for accurate protein structure prediction.
Integration of Multiple Sequence Alignments and Structural Templates: Utilized multiple sequence alignments and structural templates to enhance the accuracy of protein folding predictions, effectively capturing evolutionary relationships and structural constraints.
Deployment of End-to-End Inference Pipeline: Established a streamlined inference pipeline capable of processing raw protein sequences to generate precise 3D structural models, facilitating practical applications in computational biology
Variational Autoencoder for Anomaly Detection: Implemented the Variational Autoencoder (VAE) using Python and PyTorch to reconstruct high-dimensional data and identify anomalies through reconstruction errors.
Feature Extraction and Classification: Utilized ResNet-based feature extraction combined with K-Nearest Neighbors (KNN) for accurate anomaly classification, leveraging pre-trained networks for better feature representation.
Anomaly Localization with PatchCore: Applied the PatchCore method for effective anomaly localization, identifying defective regions within industrial images to support manufacturing quality control.
Deep Learning-Based Chicken Disease Classification: The project uses deep learning techniques to preprocess image data and train a model to classify chicken diseases. It incorporates a pipeline for data preprocessing and model training with version control managed via DVC.
CI/CD Integration and Containerization: GitHub Actions automates continuous integration and deployment, using Docker for containerization. The deployment workflows ensure seamless building, testing, and running of the application in cloud environments.
Cloud Deployment on AWS and Azure: The project supports deployment on AWS EC2 and Azure Web App. AWS deployment involves utilizing ECR for Docker image storage and EC2 for container execution, while Azure deployment leverages the Azure Container Registry and Web App Server for hosting the application.
Exploratory Data Analysis (EDA): The project analyzes math score distributions across demographics, identifies correlations with socio-economic factors, and detects patterns and outliers to inform feature selection.
Data Transformation Pipeline: A preprocessing pipeline handles missing values, scales numerical data, and encodes categorical features using ColumnTrans former, ensuring high-quality and consistent input for machine learning.
Model Training and Deployment: Models are trained and optimized via Grid Search, evaluated with MAE and RMSE, and prepared for redeployment on AWS Elastic Beanstalk
Advanced PDF Information Retrieval with Semantic Understanding: Implemented a robust pipeline for extracting and preprocessing content from PDFs, utilizing Optical Character Recognition (OCR) for scanned documents and embedding-based methods to semantically structure data. This ensures accurate alignment with large language models (LLMs) for advanced query answering.
Contextual Compression for Optimal Data Ingestion: Designed and integrated contextual summarization techniques, leveraging transformer-based architectures to reduce irrelevant data and noise while retaining critical semantic context. This preprocessing step enhances the efficiency and relevance of inputs fed into LLMs.
Enhanced Query Response via Adaptive Data Preprocessing: Combined hierarchical embeddings with fine-tuned LLMs to dynamically extract and rank document segments based on user query intent, enabling precise, context-aware answers directly from large PDF datasets.
In-Depth Exploratory Data Analysis (EDA): Conducted thorough EDA to uncover data patterns and relationships, forming the foundation for effective feature engineering and model development.
Advanced Feature Engineering and Model Validation: Developed meaningful features and implemented a robust model with rigorous testing and validation, ensuring high predictive performance.
MLOps Integration with ZenML and MLflow: Utilized ZenML and MLflow for seamless experiment tracking and deployment, exemplifying proficiency in modern MLOps practices.
Custom Implementation of AlphaFold Architecture: Recreated the AlphaFold model using Python and PyTorch, focusing on the intricate neural network layers and operations essential for accurate protein structure prediction.
Integration of Multiple Sequence Alignments and Structural Templates: Utilized multiple sequence alignments and structural templates to enhance the accuracy of protein folding predictions, effectively capturing evolutionary relationships and structural constraints.
Deployment of End-to-End Inference Pipeline: Established a streamlined inference pipeline capable of processing raw protein sequences to generate precise 3D structural models, facilitating practical applications in computational biology
Variational Autoencoder for Anomaly Detection: Implemented the Variational Autoencoder (VAE) using Python and PyTorch to reconstruct high-dimensional data and identify anomalies through reconstruction errors.
Feature Extraction and Classification: Utilized ResNet-based feature extraction combined with K-Nearest Neighbors (KNN) for accurate anomaly classification, leveraging pre-trained networks for better feature representation.
Anomaly Localization with PatchCore: Applied the PatchCore method for effective anomaly localization, identifying defective regions within industrial images to support manufacturing quality control.
See What My Client's Are Saying
Aayon's interactive Tableau dashboards were a game-changer for our sales and marketing teams. His ability to visualize real-time customer behavior empowered data-driven decision-making, leading to a 12% boost in cross-selling opportunities. Aayon truly excels at bridging the gap between data and actionable insights
Aayon is an exceptional data scientist who combines technical prowess with strategic insight. At Keen & Able, his predictive analytics project reduced customer churn by 18%, showcasing his ability to drive impactful solutions across departments. His expertise in Python and Scikit-learn is unmatched, making him a true asset to any team.
Aayon's project on chicken disease classification is a testament to his creativity and technical expertise. Using TensorFlow and CI/CD pipelines, he delivered a scalable and reliable deep learning solution. His dedication to deploying impactful models sets him apart as a forward-thinking data scientist.
Aayon's collaborative spirit and problem-solving skills made a tremendous impact at Keen & Able. His implementation of A/B testing frameworks improved customer engagement by 15% and drove an 8% revenue increase. His ability to work across teams is truly inspiring.
Working with Aayon was a transformative experience. His collaboration with data engineers to build and optimize ETL pipelines significantly improved processing times by 35%. His innovative approach and mastery of SQL, Airflow, and AWS turned complex data challenges into seamless solutions.
Denoising Diffusion Probabilistic Models (DDPMs) leverage Markov chains to reverse Gaussian noise, achieving high-quality generative tasks. Latent Diffusion Models (LDMs) optimize this by operating in compressed latent spaces, reducing computational costs while maintaining fidelity. These approaches enable versatile applications like image synthesis, super-resolution, and text-to-image generation
Llama 3 introduces large-scale transformer models (up to 405B parameters) for multilingual and multimodal AI tasks. Enhanced with rigorous data preprocessing, scaling, and safety measures, it matches state-of-the-art benchmarks for language and reasoning capabilities. Ongoing multimodal integrations (image, video, speech) expand the foundational scope of AI applications
Both diffusion models and Llama 3 emphasize scalability in terms of data, architecture, and computational resources. Innovations include latent space optimizations in diffusion models and advanced scaling laws in Llama 3 to balance model size and compute efficiency
Enter the World of Interactive Space
Embark on an intergalactic journey where you are the captain of a data exploration rocket, navigating through the mysterious realms of the DataVerse. Each planet holds secrets of data science waiting to be uncovered—patterns, clusters, algorithms, and more! Collect knowledge, solve challenges, and unlock new galaxies as you hone your data science skills.
Python, R, SQL, C/C++
CNN, RNN, Clustering, XGBoost, Graph Learning, Explainable AI (XAI), Forecasting, Image Processing, NLP
Tableau, PowerBI, D3.js, Matplotlib, T-tests, ANOVA, Regression Analysis
AWS, Azure, Docker, Kubernetes, Apache Airflow, CI/CD, Git
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