Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs.
Ce cours fait partie de la Spécialisation Building Cloud Computing Solutions at Scale
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Spécialisation Building Cloud Computing Solutions at ScaleUniversité DukeÀ propos de ce cours
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Dates limites flexibles
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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Niveau intermédiaire
Beginner level Linux and Python skills
Approx. 12Â heures pour terminer
Anglais
Sous-titres : Anglais
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Niveau intermédiaire
Beginner level Linux and Python skills
Approx. 12Â heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Programme de cours : ce que vous apprendrez dans ce cours
4Â heures pour terminer
Getting Started with Machine Learning Engineering
This week, you will learn about the methodologies involved in Machine Learning Engineering. By the end of the week, you will be able to develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications.
4Â heures pour terminer
15 vidéos (Total 47 min), 5 lectures, 1 quiz
4Â heures pour terminer
Using AutoML
This week, you will learn about AutoML and how to use it to build efficient Machine Learning solutions with little to no code. These technologies include Ludwig, Google AutoML, Apple Create ML and Azure Machine Learning Studio. You will apply these solutions by using both open source and Cloud AutoML technology.
4Â heures pour terminer
21 vidéos (Total 138 min), 2 lectures, 1 quiz
5Â heures pour terminer
Emerging Topics in Machine Learning
This week, you will learn MLOps strategies and best practices in designing Cloud solutions. Then, you will explore Edge Machine Learning and how to use AI APIs. You will apply these strategies to build a low code or no code Cloud solution that performs Natural Language Processing or Computer Vision.
5Â heures pour terminer
22 vidéos (Total 158 min), 2 lectures, 1 quiz
Avis
- 5 stars75,67Â %
- 4 stars13,51Â %
- 3 stars8,10Â %
- 2 stars2,70Â %
Meilleurs avis pour CLOUD MACHINE LEARNING ENGINEERING AND MLOPS
par JY1 juin 2022
Great course to know practical ideas and concepts.
par SG8 juil. 2021
Excellent course, very concise but complete, if possible a second version would be ideal
À propos du Spécialisation Building Cloud Computing Solutions at Scale
With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies.
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