Building Real-Time ML Pipelines the Easy Way

This course explains how to build real-time operational machine learning pipelines using streaming and batch data, feature stores, serverless processing, and MLOps automation. Demonstrations include churn prediction, fraud detection, model deployment, and production monitoring for drift.

Syllabus

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