With the rapid growth of ML applications and AI, it is evident that developing an accurate model represents only a portion of the overall solution. To successfully build a Machine Learning-driven product, one 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 stored.
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