Given the surge in ML applications and AI, it is evident that developing an accurate model is merely one part of the challenge. To successfully create a product driven by Machine Learning, one 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 artifact storage.
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