With the rapid growth of ML applications and AI, it is evident that building an accurate model is only part of the challenge. 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 include:
- MLOps tools
- Model drift and monitoring
- Seamless retraining and model versioning
- Data versioning as well as artefact storage
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