Machine Learning Engineering for Production (MLOps) Specialization

Machine Learning Engineering for Production (MLOps) Specialization

Machine Learning Engineering for Production (MLOps) Specialization Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities. Taught in English 22 languages available Some content may not be translated Instructors: Andrew Ng +3 more Close Instructors Top Instructor Andrew Ng DeepLearning.AI 42 Courses • 7,117,154 learners Top Instructor Cristian

Description

In the rapidly evolving field of machine learning, there is a growing demand for professionals with expertise in deploying and maintaining ML models at scale. Machine Learning Engineering for Production (MLOps) is a specialization that focuses on the intersection of machine learning and software engineering to enable seamless deployment of ML models in production environments.

MLOps professionals are responsible for developing, testing, and deploying machine learning models in real-world applications. They possess a deep understanding of data science, software engineering, and cloud computing to ensure that ML models are reliable, scalable, and efficient. MLOps specialists work closely with data scientists, engineers, and business stakeholders to translate ML models into actionable insights that drive business value.

The MLOps specialization is designed to equip professionals with the skills and knowledge needed to succeed

Machine Learning Engineering for Production (MLOps) Specialization

Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.

Taught in English

Some content may not be translated

Andrew Ng
Cristian Bartolomé Arámburu
Laurence Moroney

Instructors: Andrew Ng

Top Instructor

67,818 already enrolled

Specialization – 4 course series

Get in-depth knowledge of a subject

4.6

(3,341 reviews)

Advanced level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you’ll learn

  • 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.

Details to know

Shareable certificate

Add to your LinkedIn profile

,

See how employees at top companies are mastering in-demand skills

About the author

Study on Scholarship Today -- Check your eligibility for up to 100% scholarship.