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MLOPs Lifecycles

slimdestro profile image D\sTro ・1 min read

MLOPs life cycle 🔃 ⤵

  • Define business need

  • Getting datasets ready. This phase includes data cleaning, labelling, pixel optimisation, open source datasets and enterprise data-lakes

  • Model development (code part)

  • Training and optimisations of model

  • Deployment(uat/prod)

  • User interface and API development to let user interact with the model

  • Monitoring both model and system resources

  • Insights and analytics

  • Continuous model training: deployed one time model can work for few time-frames and hence it needs retraining on new data

  • world has changed. We use CI/CD tools like Jenkins and Docker/cubectl for code automation

  • Security and protection of ML model against known vulnerability. This is the point most people ignore

-Thanks

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