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Repository contains the detail about ML model deployment and building end-to-end ML pipeline for production

Jupyter Notebook 13.00% Python 83.48% HTML 2.06% CSS 1.17% JavaScript 0.29%
ml-pipeline ml-production mlflow mlops mlops-workflow apache-airflow onnx onnxruntime deepchecks evidentlyai

mlops's Introduction

Provide detail and implementation of different tools/libraries/frameworks for-

  • Experiment Tracking
  • Model Evaluation
  • Model Packaging
  • Model Versioning
  • Model Deployment
  • Model Monitoring
  • Model Orchestration and pipelining

Idea here is to provide the detailed implementation of building end-to-end ML pipeline and cover all the specifics about deploying the ML model in production and How to maintain it after that.

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