Implementing End-to-End Demand Forecasting with Databricks and MLflow

Implementing End-to-End Demand Forecasting with Databricks and MLflow

Databricks via YouTube Direct link

Ingesting the files

3 of 16

3 of 16

Ingesting the files

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Implementing End-to-End Demand Forecasting with Databricks and MLflow

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

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