ML Engineering
Productionizing ML: pipelines, deployment, monitoring, and reliability.
- Home
- ML Engineering
MLOps• advanced
ML Engineering
Productionizing ML: pipelines, deployment, monitoring, and reliability.
4.4(56 reviews)
112 students
Last updated 2024-01-22
James Wilson
Senior ML Engineer
0:00 / 0:00
ML Engineering - Preview
What you'll learn
Design and implement ML pipelines
Deploy ML models to production
Monitor and maintain ML systems
Implement CI/CD for ML workflows
Scale ML systems for high traffic
Course content
3 sections • 57 minutes
1
Pipelines
From data to serving.
21 min
2
Deployment
Batch vs real-time.
19 min
3
Monitoring
Drift and alerts.
17 min
Requirements
- Strong Python programming skills
- Experience with ML frameworks (TensorFlow/PyTorch)
- Understanding of cloud platforms (AWS/GCP/Azure)
- Basic knowledge of Docker and Kubernetes
About this course
This advanced course covers everything needed to take ML models from prototype to production. You'll learn industry best practices and work with real production scenarios.
$299$399
25% OFF
One-time payment • Lifetime access
6 hours of video content
25 downloadable resources
Certificate of completion
Lifetime access
Production-ready code
1-on-1 mentoring sessions
30-day guarantee
Full refund if you're not satisfied
Course Info
English
57 hours total
3 lessons
Certificate of completion