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  1. Home
  2. Learning Paths

Machine Learning Engineering for Production (MLOps) Learning Path

Machine Learning Engineering for Production (MLOps) is a Specialization designed by Coursera. It consists of four courses and can be completed in four months .

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Description

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.

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Free Learning Paths by Career Track

Bootstrap Data ScientistBootstrap Data AnalystBootstrap Business AnalystBootstrap Data EngineerBootstrap Chief Data OfficerBootstrap Data Architect

Included Courses

Coursera
DeepLearning.AI
Free
1. Introduction to Machine Learning in Production
Andrew Ng and Cristian Bartolomé Arámburu

10 hours

Advanced

39,838
Artificial Intelligence Big Data Classification Data Analysis Data Modeling
Coursera
DeepLearning.AI
Free
2. Machine Learning Data Lifecycle in Production
Robert Crowe

22 hours

Advanced

15,559
Tensorflow Artificial Intelligence Data Cleaning Data Collection Data Modeling Data Pipelines
Coursera
DeepLearning.AI
Free
3. Machine Learning Modeling Pipelines in Production
Robert Crowe

26 hours

Advanced

8,564
Keras Tensorflow Algorithms Artificial Intelligence AutoML Data Analysis Data Analytics
Coursera
DeepLearning.AI
Free
4. Deploying Machine Learning Models in Production
Laurence Moroney and Robert Crowe

33 hours

Advanced

7,901
Docker Github Kubernetes Tensorflow Artificial Intelligence Automation Cloud Computing Data Modeling Data Privacy
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