An MLOps pipeline on semiconductor quality prediction. The projects contains semiconductor sensor data and classifies the end product as Pass or Fail. There are ~580 sensor features that are used. Stack used: MLflow, Prefect, AWS S3, Docker, MongoDB, EvidentlyAI, Prometheus, Grafana
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A batch and stream process for tracking Youtube's API stats. Stack used: Kafka, Prefect, dbt, GCP Storage, BigQuery, Terraform
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Built system that uses a machine learning model as well as a collaboration of different user modes that receive user's input data and project optimal assembly step sequences.
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I conducted root cause analysis for a quality checking machine issue by querying sensor data, analyzing it, and visualizing findings to identify malfunctioning parts. To prevent future problems, I implemented statistical process control measures for early detection on the assembly line.
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To improve wafer production, ASML utilized spectroscopy image data to estimate the size of dust particles on their reticles. By creating and testing new features, I optimized the estimation process, leading to more accurate predictions using ML models.
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The analysis examined factors impacting profit margins, pricing strategies, and customer relationships, using PCA to reduce the dimensionality of the dataset and clustering techniques to identify distinct customer segments, each with specific actionable insights to enhance profitability.
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