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Conclusion
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Classroom Contents
Implementing End-to-End Demand Forecasting with Databricks and MLflow
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- 1 Outline
- 2 Scope of Project
- 3 Ingesting the files
- 4 Using extra data sources
- 5 Feature Engineering
- 6 Picking a ML model
- 7 Model Granularity
- 8 Making use of parallelism
- 9 Tracking Performance and experiments
- 10 Which metrics to use?
- 11 Working with reliability buckets
- 12 Feeding it back into the client's systems
- 13 Frequency of training and scoring
- 14 Monitoring and Alerting
- 15 Conclusion
- 16 DATA+AI SUMMIT 2022
