With the boom of ML applications and AI, it is clear that developing an accurate model is only a piece of the puzzle. To successfully create a Machine Learning driven product, one must create MLops practices and infrastructure to train, deploy and manage ML models in production. Some key topics include:
- MLops tools
- Model drift and monitoring
- Seamless retraining and model versioning
- Data versioning as well as artefact stored.
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