Approximately 80% of data science ML projects never make it to production despite an increase in investments in ML-enabled applications.According to Gartner, although organisations are keen to apply DevOps principles and practices for AI and ML projects, they lack the skills and experience to design and implement a fully automated ML pipeline solution.
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An automated ML training pipeline increases the frequency of ML experiments which leads to rapid innovation and shortens the time taken to bring models to production.
Implementing MLOps lays the foundation for data scientists to collaborate with software engineers and IT professionals on the development and deployment of machine learning models to production
Having a model governance framework and also versioning ML models as part of MLOps implementations enables data science teams to reproduce experiments and trained models.
Implementing model monitoring as part of MLOps solutions protects against data and model drift.
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