A selection of projects spanning machine learning, computer vision, and data engineering.
Featured
End-to-End ML Pipeline with SageMaker Pipelines
MLA four-step SageMaker Pipeline covering data preprocessing, XGBoost model training, model creation, and batch inference — fully orchestrated and reproducible.
ML Workflow Orchestration with AWS Step Functions
Data EngOrchestrating SageMaker Processing, Training, and Inference jobs end-to-end using AWS Step Functions state machines.
All Projects
SageMaker Jobs: Processing, Training, Inference & HPT
MLHands-on walkthrough of SageMaker's four core job types — Processing, Training, Batch Transform, and Hyperparameter Tuning — on a retail sales dataset.
Deploying an ML Model with a SageMaker Real-Time Endpoint
MLEnd-to-end deployment of a LightGBM bank churn model as a SageMaker real-time endpoint — custom Docker inference server, ECR, and a Lambda function for automated S3-triggered inference.
SQL from Scratch — Tutorial Series
TeachingA structured nine-part SQL series covering relational databases, queries, joins, aggregation, subqueries, and schema design with real datasets.