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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