The video discusses the standard development life cycle steps for implementing a machine learning model, including crisp MLQ and crisp DM.
Continuous integration and continuous development are important steps in the process.
Monitoring and maintenance are necessary for supporting the model once it's in production due to potential data drift and performance skew.
MLOps standards are introduced as a way to implement and deploy models effectively, with considerations for productionizing, implementing pipelines, and packaging the code.
Important keywords and concepts mentioned include code repository, retraining, CI/CD, packaging, success criteria, and quality. Data validation, versioning, feature engineering, and model drift are essential for monitoring and comparing models and data changes.
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