Recommender Sandbox
Session-Level Interest Vectorizer & Cosine Retrieval
Session-Level Recommender Sandbox
Simulate ad/course personalization. Select items below to build your session click history. The recommender dynamically creates a real-time interest vector and serves recommendations instantly.
Item Catalog (Click to interact)
Advanced Time Series Forecasting
Learn stacked regressor pipelines, seasonality adjustments, and PSI drift monitoring.
LLM Prompt Engineering & RAG
Master prompt templates, dense embeddings search, and semantic routing architectures.
Vector Databases & Semantic Search
Index high-dimensional vectors, optimize Elasticsearch index maps, and measure NDCG metrics.
Data Pipelines at Scale with PySpark
Distribute computations, manage large SQL data warehouses, and build ETL jobs.
MLOps: Deployment & Pipelines
Track model experiments with MLflow, containerize with Docker, and run auto-retraining.
Statistics & A/B Testing
Formulate hypotheses, calculate sample sizes, and evaluate A/B experiments.
Dynamic Recommendations (Real-Time Output)
No items in session history.
Click items in the catalog to emulate clickstream data and view live recommendations.