Projects

Ai-ml Projects

Browse my ai-ml work tagged with "AWS".

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All Tags admin-dashboard anomaly-detection API-integration authentication automation AWS azure benchmarking big-data browser-automation churn-prediction CI-CD classification CLI clustering computer-vision CRUD customer-segmentation data-analysis data-pipeline dataclasses deep-learning deployment Docker docker email explainability fastapi feature-engineering finance fine-tuning flask game generative-ai guardrails GUI hugging-face huggingface hypothesis-testing image-classification IQR-capping jupyter label-encoding LLM log-analysis logistic-regression machine-learning min-max-scaling mlflow mlops MLOps model-registry neural-network NLP OOP pandas productivity prompt-engineering pyspark python RAG regression resampling REST-API retention-strategy scikit-learn scipy scope scripting semantic-search sentiment-analysis seq2seq serverless shap slack statistical-analysis tabular-data terminal terminal-ux text-classification text-generation time-series transfer-learning transformer unsupervised-learning vector-database visualisation web-app web-scraping xgboost zero-shot

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AWS Lambda & Amazon Bedrock: Benchmarking TF-IDF, LSTM, and Zero-Shot LLM Sentiment Analysis

Ai-ml

A full ML pipeline benchmarking TF-IDF + Logistic Regression, a Keras LSTM, and Claude Haiku via Amazon Bedrock on 50,000 IMDb reviews. The winning model is deployed live as a serverless REST API on AWS Lambda with a Flask frontend.

NLP machine-learning deep-learning deployment flask REST-API data-analysis visualisation python web-app LLM sentiment-analysis serverless AWS Docker text-classification benchmarking

GitHub Live Details

LLM Document Summarizer — LLMOps with Amazon Bedrock

Ai-ml

PDF document summarizer using Amazon Bedrock (Claude Haiku) with production LLMOps patterns: versioned YAML prompt templates, automated ROUGE evaluation, Bedrock Guardrails for PII redaction, and MLflow tracking of every inference call. Deployed on Azure App Service.

LLM AWS azure deployment web-app benchmarking jupyter REST-API docker CI-CD NLP prompt-engineering mlops guardrails

GitHub Live Details

SMS Spam Classifier

Ai-ml

SMS spam detection comparing three approaches — Naive Bayes, SVM, and Amazon Bedrock (Claude Haiku zero-shot) — on the same test set. Every prediction comes with a plain-English explanation of the words that drove it. Class imbalance addressed explicitly. Deployed on Azure App Service.

classification machine-learning NLP text-classification flask azure deployment explainability resampling zero-shot jupyter scikit-learn benchmarking AWS

GitHub Live Details

Stock Trend Pattern Recognition — Amazon SageMaker MLOps

Ai-ml

Stacked LSTM for stock trend pattern detection, served via Amazon SageMaker Real-Time Endpoint. Flask frontend on Azure App Service — multi-cloud MLOps. Pattern detection only, not financial advice.

MLOps deep-learning time-series neural-network LLM benchmarking AWS azure deployment flask web-app zero-shot finance model-registry jupyter

GitHub Live Details

Ai-ml Projects

AWS Lambda & Amazon Bedrock: Benchmarking TF-IDF, LSTM, and Zero-Shot LLM Sentiment Analysis

LLM Document Summarizer — LLMOps with Amazon Bedrock

SMS Spam Classifier

Stock Trend Pattern Recognition — Amazon SageMaker MLOps

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