Recently, Andrew Ng took to the professional networking platform to announce that the Specialization on Machine Learning Engineering for Production (MLOps) by DeepLearning.AI is now available on Coursera. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production.
NG posted, “I’m thrilled DeepLearning.AI’s Machine Learning Engineering for Production (MLOps) specialization is now available on Coursera!”
“Being able to train ML models is essential. And, to build an effective AI career, you need production engineering skills as well, to build and deploy ML systems. With this specialization, you can grow your knowledge of ML into production-ready skills” the AI expert added.
In striking contrast with standard machine learning modelling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.
The instructors of this course are Robert Crowe, TensorFlow Developer Engineer at Google, Laurence Moroney, who is the Lead AI Advocate at Google and Andrew Ng. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. Some of the important topics that you will learn here include-
- Designing a machine learning production system end-to-end.
- Establish a model baseline, address concept drift and prototype, how to develop, deploy and continuously improve a productionized machine learning application.
- You will be building data pipelines by collecting, cleaning as well as validating datasets. You will also learn how to establish a data lifecycle by using data lineage and provenance metadata tools.
- Lastly, you will learn to apply the best practices and progressive delivery techniques to maintain as well as monitor a continuous production system.
By the end of this course, you will be ready to perform several interesting tasks, such as designing an end-to-end ML production system, build data pipelines, establish data lifecycle, apply techniques to manage modelling resources, use analytics to address the fairness of ML models, implement feature engineering, deliver deployment pipelines for model serving and more.
The course will take approximately 3 months to complete and you can also earn a certificate upon completion of the course.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.