- Model Pipelines
- Deployment Pipelines
- Machine Learning Engineering for Production
- Managing Machine Learning Production Systems
- Data Pipelines
March 16, 2023
Approximately 2 months at 10 hours a week to completeELIAS LIMOUNI's account is verified. Coursera certifies their successful completion of DeepLearning.AI Machine Learning Engineering for Production (MLOps) Specialization.
Course Certificates Completed
Introduction to Machine Learning in Production
Machine Learning Data Lifecycle in Production
Machine Learning Modeling Pipelines in Production
Deploying Machine Learning Models in Production
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
Earned after completing each course in the Specialization
DeepLearning.AI
Taught by: Andrew Ng & Cristian Bartolomé Arámburu
Completed by: ELIAS LIMOUNI by December 14, 2022
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
DeepLearning.AI
Taught by: Robert Crowe
Completed by: ELIAS LIMOUNI by February 1, 2023
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
DeepLearning.AI
Taught by: Robert Crowe
Completed by: ELIAS LIMOUNI by March 9, 2023
At the rate of 5 hours a week, it typically takes 5 weeks to complete this course
DeepLearning.AI
Taught by: Laurence Moroney & Robert Crowe
Completed by: ELIAS LIMOUNI by March 16, 2023
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course