Deploying Scalable Machine Learning for Data Science
Become a Machine Learning Specialist,
Machine learning models often run in complex production environments that can adapt to the ebb and flow of big data. The tools and practices that help data scientists rapidly
Contents:
- Introduction
- 1. The Need to Scale ML Models
- 2. Design Patterns for Scalable ML Applications
- 3. Deploying ML Models as Services
- 4. Running ML Services in Containers
- 5. Scaling ML Services with Kubernetes
- 6. ML Services in Production
- Conclusion
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| Rating | Not enough ratings |
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| Length | 1h 43m |
| Starts | On Demand (Start anytime) |
| Cost | $29/month (Access to entire library- free trial available) |
| From | LinkedIn Learning |
| Instructor | Dan Sullivan |
| Download Videos | Only via the LinkedIn Learning mobile app |
| Language | English |
| Subjects | Programming Data Science IT & Networking |
| Tags | Machine Learning IT Big Data Docker Products |
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| Rating | Not enough ratings |
|---|---|
| Length | 1h 43m |
| Starts | On Demand (Start anytime) |
| Cost | $29/month (Access to entire library- free trial available) |
| From | LinkedIn Learning |
| Instructor | Dan Sullivan |
| Download Videos | Only via the LinkedIn Learning mobile app |
| Language | English |
| Subjects | Programming Data Science IT & Networking |
| Tags | Machine Learning IT Big Data Docker Products |
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