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Model Deployment Challenges and Best Practices - Webinar for Analytics Vidhya
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Classroom Contents
MLOps - Machine Learning Operations
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- 1 An Introduction to MLOps
- 2 MLOps with Azure - Hands on Session
- 3 Hands on Kubernetes for Data Scientists and Engineers
- 4 MLOps with TFX pipelines - Tensorflow Extended
- 5 MLOps - End to End automated CI/CD pipeline for Continuous Deployment
- 6 Feature Store for Machine Learning - MLOps
- 7 MLOps with Feature Store - Move models from development to production
- 8 Accelerating Machine Learning with a Feature Store
- 9 Model Monitoring - Concept and Data Drift - Part 2
- 10 Model Monitoring Deep Dive
- 11 Machine Learning Models - Load and Performance Testing Demo
- 12 Model Deployment Deep Dive using Containers, Google Cloud Run and App Engine
- 13 Model Deployment Challenges and Best Practices - Webinar for Analytics Vidhya
- 14 MLOps - Machine Learning Deployment with CI/CD pipeline - Part 1
- 15 MLOps - GitHub Kubernetes Continuous Model Deployment - Streamlit - Part 2
- 16 Getting Started with Apache Airflow
