With the surge in machine learning applications and artificial intelligence, it is evident that developing an accurate model is merely one part of the solution. To successfully create a machine learning-driven product, organisations must establish MLOps practices and infrastructure to train, deploy, and manage ML models in production. Key topics covered include:
- MLOps tools
- Model drift and monitoring
- Seamless retraining and model versioning
- Data versioning as well as artefact storage.
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