With the rapid expansion of ML applications and AI, it is evident that developing an accurate model is merely one part of the larger picture. To successfully build Machine Learning driven products, it is essential to establish MLOps practices and infrastructure capable of training, deploying, and managing ML models in production. Key areas of focus include:
- MLOps tools
- Model drift and monitoring
- Seamless retraining and model versioning
- Data versioning as well as artefact stored.
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