Building Real-Time ML Pipelines the Easy Way

Building Real-Time ML Pipelines the Easy Way

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Feature Used For The Model (Example)

8 of 16

8 of 16

Feature Used For The Model (Example)

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Classroom Contents

Building Real-Time ML Pipelines the Easy Way

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  1. 1 Intro
  2. 2 Most AI Projects Never Make it to Production
  3. 3 Operationalizing Machine Learning is Challenging
  4. 4 Resource Intensive Processes, Data & Org Silos
  5. 5 Serverless Simplicity With Maximum Performance
  6. 6 Accelerate Development & Deployment With an Integrated Feature-Store
  7. 7 Churn Prediction Example: Raw Data Model
  8. 8 Feature Used For The Model (Example)
  9. 9 Implementing A SINGLE Feature Using SQL
  10. 10 Kappa Architecture - Intro
  11. 11 Serverless Stream Processing For Real-Time & Batch
  12. 12 Faster development to production through MLOps & Serverless automation
  13. 13 Rapid Deployment of Real-Time Serverless Pipelines
  14. 14 Glue-less Model Monitoring and Governance
  15. 15 ML Pipeline Example: Predicting Financial Fraud
  16. 16 MLOps for Good Hackathon

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