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Fully Automated MLOps Pipeline – Part 2
- The article discusses the completion of a fully automated MLOps pipeline for a forecasting model, focusing on monitoring and automated retraining processes.
- The model used the Amazon DeepAR Forecasting Algorithm to forecast 30 data points, with the mean quantile loss metric used for evaluating accuracy.
- Model monitoring is facilitated through AWS CodePipeline and SageMaker's built-in monitoring capabilities, focusing on model quality monitoring.
- Limitations of the built-in monitoring include constraints related to datasets, limited metrics, scheduling restrictions, and lack of event-triggered alarms.
- Custom solutions were developed to address the monitoring limitations, involving a custom SageMaker Pipeline, CloudWatch metrics, and alarms.
- For model retraining, a custom solution was developed using a combination of monitoring processes, Lambda functions, and CloudWatch Dashboards.
- The article emphasizes the transition from experimentation to operation in MLOps, highlighting the importance of utilizing feature-rich services like AWS SageMaker.
- Despite limitations, SageMaker offers powerful tools for data science projects, and there are still unexplored monitoring features like data quality and drift detection.
- Overall, effectively managing the data science lifecycle involves balancing experimentation and operationalization while leveraging platform features for enhanced project efficiency.
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