MLOps Architecture for Real-Time Fraud Detection
An AI-driven solution for real-time credit card fraud detection using MLOps techniques
Fully orchestrated pipelines, including data ingestion, model training, and real-time prediction and monitoring
High fraud case detection through Graph Convolutional Network, XGBoost, and CatBoost models
Tech: Neo4j graph DB, Sklearn, PyG, Mlflow, Kafka, Grafana, Mage orchestration, Docker, Streamlit
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Transaction Stream Data Engineering Pipeline
Generate transaction data via Stripe's API
Stream data using Apache Kafka and process it in real-time with PySpark Structured Streaming
Store processed data in PostgreSQL
Manage data transformations and modeling using dbt
Visualize data using Grafana
Tech: PostgreSQL DB, Kafka, PySpark, dbt, Grafana
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Your Personal Finance Voice Assistant
Chat, Talk, Learn and Analyse your spending habits with your Personal Finance Assistant
Communicate through text or speech
Ask follow-up questions (the language model can see the chat history)
On the dev side, see how the RAG is performing by analysing prompts and retrieved information
Tech: SQLite, OpenAI, HuggingFace, LlamaIndex, Azire Pheonix monitoring, Streamlit
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Glaswegian Audio Dataset and ASR model
Co-create a 120 minute open-sourced Glaswegian dataset
Preprocess raw audio and transcriptions and upload to HuggingFace
Research into audio AI models and fine-tune ASR and TTS models
Tech: HuggingFace, Python, Fine-Tuning, Audio AI
View on HuggingFace
MLOps Architecture for Insurance Fraud Detection
Building an end-to-end MLOps pipeline to detect car insurance fraud
Pipeline orchestration covering data storage, data preprocessing (using IV and WoE), model training, deployment, and monitoring
Focus on achieving high recall in fraud detection using a Balanced Random Forest Classifier
Tech: PostgreSQL DB, Terraform, Google Cloud Platform, Mlflow, Prefect, Grafana, Evidently, Docker, FastAPI
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Lending Club Data Engineering Pipeline
Build a data pipeline to process and visualize Lending Club data
Extract raw data from Kaggle and load it into Google Cloud Storage
Process data with dbt in BigQuery
Create visualizations using Looker
Manage infrastructure with Terraform
Orchestrate the entire process with Mage
Tech: Docker, Mage orchestration, Google Cloud Platform, Terraform, dbt, Looker
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