Developed an end-to-end machine learning solution for predicting employee churn using Azure Databricks, leveraging Spark for data processing, MLflow for managing the ML workflow, and deploying the model using Databricks model serving.
George: Learning to Place Long-Lived Containers in Large Clusters with Operation Constraints
Model Serving with FastAPI
Minimal MLOps regression skeleton (California Housing) with training pipeline, Evidently drift/performance report, FastAPI prediction service, Dockerized training/serving environments, ready for CI/CD extension
Lightweight model serving in Trusted Execution Environments.
End-to-end Titanic survival prediction pipeline built with Python and Databricks, including Delta Lake, SQL, feature engineering, and machine learning model comparison.
App uses live GCP token usage metrics by model to to demonstrate true GSU capacity, recovery and cost
ML Runtime Experiments in Rust
This repository integrates ESM, EvolutionaryScale's protein language models, with the ExoStack platform to enable scalable AI-driven protein analysis, inference, and deployment.
Compare inference performance of ML models across C++ vs Python
Distributed tracing for a ML deployment using OpenTelemetry (OTel) and eBPF for end-to-end observability
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