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