Given the rapid growth of machine learning applications and artificial intelligence, it is evident that developing an accurate model is merely one part of the equation. To successfully deliver a machine learning-driven product, organisations must establish robust MLOps practices and infrastructure capable of training, deploying, and managing ML models in production. Key topics covered include:
- MLOps tools
- Model drift and monitoring
- Seamless retraining and model versioning
- Data versioning along with artefact storage
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