Given the rapid growth of ML applications and AI, it is evident that developing an accurate model is only one part of the solution. To successfully build a Machine Learning-driven product, organizations 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 and artefact storage
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