Data Science & AIMachine LearningAdvanced
MLOps: Deploying and Monitoring Models
Get models out of notebooks and keep them healthy in production.
4.8(233 ratings) 60 lessons · 8 modules 21h940 students
PN
What you'll learn
- Package and serve a model as a versioned service
- Eliminate training/serving feature skew
- Monitor for data and concept drift
- Roll back a bad model without downtime
About this course
The gap between a trained model and a reliable service is where most ML projects die. This course covers packaging, serving, feature consistency, monitoring for drift, and rolling back a model that has gone bad at 2am.
Curriculum
8 modules · 60 lessons · 21h
The detailed curriculum for this course is being finalised. The outline covers 60 lessons across 8 modules.
Requirements
- Python and basic ML experience
- Docker familiarity
Who this course is for
- ML engineers
- Data scientists owning production models
Student reviews
No written reviews yet. The course has 233 ratings with an average of 4.8 out of 5.
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