Machine Learning Engineering for Production (MLOps)

Completed by Sayak Paul

September 19, 2021

Approximately 16 weeks to complete.

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

View certificate for Sayak Paul, Machine Learning Engineering for Production (MLOps), offered through Coursera. Congratulations! You have completed all four courses of Machine Learning Engineering for Production (MLOps) Specialization.

In this Specialization, you learned how to conceptualize and maintain integrated systems. You mastered well-established tools and methodologies to build production systems that can handle relentless evolving data and continuously run at maximum efficiency. 

You're now familiar with the capabilities, challenges, and consequences of machine learning engineering in production and are ready to level up your career by participating in the development of leading-edge AI technology and solving real-world problems.

Course Certificates

Earned after completing each course in the Specialization

Introduction to Machine Learning in Production

DeepLearning.AI

Taught by: Andrew Ng & Cristian Bartolomé Arámburu

Completed by: Sayak Paul by May 14, 2021

At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.

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Machine Learning Data Lifecycle in Production

DeepLearning.AI

Taught by: Robert Crowe

Completed by: Sayak Paul by May 19, 2021

At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.

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Machine Learning Modeling Pipelines in Production

DeepLearning.AI

Taught by: Robert Crowe

Completed by: Sayak Paul by July 2, 2021

At the rate of 5 hours a week, it typically takes 5 weeks to complete this course

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Deploying Machine Learning Models in Production

DeepLearning.AI

Taught by: Laurence Moroney & Robert Crowe

Completed by: Sayak Paul by September 19, 2021

At the rate of 5 hours a week, it typically takes 4 weeks to complete this course

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