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A production-grade MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.
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82
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Merge pull request #12 from deaneeth/feature/kafka-integration
42ccd97View on GitHubRefactor code structure for improved readability and maintainability.
a637fc5View on GitHubMerge pull request #11 from deaneeth/feature/kafka-integration
622814eView on GitHubRefactor Spark pipeline to include StandardScaler for feature normalization
6d28a63View on GitHubMerge pull request #10 from deaneeth/feature/kafka-integration
ab217faView on GitHubdocs: add comprehensive deliverables checklist for MLOps pipeline
ca970b4View on GitHubRefactor README and remove final summary reports
cc8ebf0View on GitHubMerge pull request #9 from deaneeth/feature/kafka-integration
76dd559View on GitHubdocs: update README with enhanced project structure and quick start guide
3685778View on GitHubMerge pull request #8 from deaneeth/feature/kafka-integration
ab91a06View on GitHubfix(ci): resolve pytest-cov and Kafka health check failures
2d918ccView on GitHubMerge pull request #7 from deaneeth/feature/kafka-integration
b5bf388View on GitHubdocs: complete README production overhaul - 100% corruption-free, optimized structure
df15ae5View on GitHub