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MLOps

Deploy, monitor, and maintain AI systems in production

12 chapters
1 free chapters
4.9 rating

Course Curriculum

🔒 Full Course (11 chapters)

02
Model Versioning
Tracking experiments
40 min
03
Data Pipelines
ETL for ML
45 min
04
Training Pipelines
Automated training
50 min
05
Model Serving
Deployment patterns
45 min
06
Monitoring & Observability
Tracking model health
40 min
07
CI/CD for ML
Continuous deployment
45 min
08
Feature Stores
Managing features
40 min
09
A/B Testing
Experimenting safely
35 min
10
Cost Optimization
Efficient ML ops
40 min
11
Security & Compliance
Secure ML systems
45 min
12
MLOps at Scale
Enterprise patterns
50 min

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What You'll Learn

MLOps Fundamentals
Model Versioning
Data Pipelines
Training Pipelines
Model Serving
Monitoring & Observability
CI/CD for ML
Feature Stores

+ 4 more chapters

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