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SWE 407: Machine Learning Systems Engineering (MLOps)
Engineering production software systems incorporating machine learning models. The ML engineering lifecycle, data versioning with DVC, model experiment tracking and registry with MLflow, automated feature stores, continuous training pipelines, low-la...
Course Overview
Engineering production software systems incorporating machine learning models. The ML engineering lifecycle, data versioning with DVC, model experiment tracking and registry with MLflow, automated feature stores, continuous training pipelines, low-la... Curriculum Structure &...
What You'll Learn
- Version machine learning datasets, model artifacts, and feature pipelines reproducibly using DVC and Git.
- Track model training experiments, hyperparameters, and metrics using MLflow.
- Deploy low-latency, scalable model inference microservices with health checks and batching.
- Monitor production models for statistical data drift and concept drift using Evidently AI.
- Build automated CI/CD retraining pipelines triggered by performance degradation.
Requirements
- SWE 104
- SWE 201
Degree Programme Curriculum Context
This course is a curriculum unit of the Bachelor of Engineering (B.Eng.) in Software Engineering degree framework in Nigeria.
Earn Your Certificate
Showcase your skills with a certificate! Complete the course, submit your project, and earn your certificate. Here's a sample of what you'll receive to show off your accomplishments.

Course Curriculum
Open-access academic curriculum. All foundational lectures available to read free.
No curriculum found.
Academic Ecosystem & Connected Pathways
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Instructor
PAMCET Learning Team
Institutional Course Curation · PAMCET
Qualifications:
PAMCET Learning Team — Institutional Course Curation, Digitpen Hub LtdMy Skills:
- Curriculum Curation
- Course Content Review
- Learning Outcome Design
- Course Publishing & Quality Assurance
- Platform Content Operations
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Course Includes
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