Amid the surge in machine learning applications and AI, it has become evident that developing an accurate model is merely one part of the solution. To successfully build a machine learning-driven product, organizations must establish 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 artifact storage
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