MLOPs Lifecycles

This content originally appeared on DEV Community and was authored by D\sTro

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

This content originally appeared on DEV Community and was authored by D\sTro

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