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Learner Reviews & Feedback for Deploying Machine Learning Models in Production by DeepLearning.AI

4.5
stars
324 ratings

About the Course

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. 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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving Introduction Week 2: Model Serving Patterns and Infrastructures Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging...

Top reviews

MB

Mar 30, 2024

Worth the effort to go through the course, since it gave me context on what I should expect to either start a new AI project, deploy an AI model into production or scale the AI service.

MN

Apr 21, 2022

This course is essential for data scientist if they want to embark on the journey of data scientist in industry. I learned a lot of useful techniques. Thank you team!

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51 - 53 of 53 Reviews for Deploying Machine Learning Models in Production

By Ashkan R

•

Jan 18, 2024

Outdated, google's oriented tools. Doesn't involve open-source guidance.

By Guido S

•

Dec 14, 2023

The syllabus is somewhat random at times and sometimes information is outdated. There is a disconnect between the complexity of the labs (what is actually done there, not the copy-paste that one has to do technically to pass) and the superficiality of the videos. What is really cool though is that actual deployments can be carried out on GCloud.

By Justin H

•

Aug 11, 2023

Two google cloud labs were broken. Forum assistant was very helpful and got it working after some down time. Nonetheless, action was taken. Kudos to him. Coursera and Google labs partner which is Qwiklabs need to get their shit together.