Deploying Machine Learning Models in Production

Deploying Machine Learning Models in Production

Deploying Machine Learning Models in Production This course is part of Machine Learning Engineering for Production (MLOps) Specialization Taught in English 22 languages available Some content may not be translated Instructors: Laurence Moroney +1 more Close Instructors Instructor ratings We asked all learners to give feedback on our instructors based on the quality of their

Description

Deploying Machine Learning Models in Production

Machine learning has evolved to become an integral part of many organizations, driving important decision-making processes and enhancing business operations. However, deploying machine learning models into production can be a complex and challenging task that requires careful planning and implementation.

One of the key considerations when deploying machine learning models in production is ensuring that the models are scalable and reliable. Models that perform well in a controlled environment during development may not necessarily perform as expected when deployed in a production environment. It is essential to thoroughly test and validate the models before deployment to ensure that they can handle the volume and complexity of real-world data.

Another important aspect of deploying machine learning models in production is monitoring and performance tracking. Once a model is deployed, it is crucial to continuously monitor its performance and make adjustments as needed

Deploying Machine Learning Models in Production

This course is part of Machine Learning Engineering for Production (MLOps) Specialization

Taught in English

Some content may not be translated

Laurence Moroney
Robert Crowe

Instructors: Laurence Moroney

32,095 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.5

(318 reviews)

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85%

Advanced level

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32 hours (approximately)
Flexible schedule
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Assessments

15 quizzes

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