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Cloud Machine Learning Engineering and MLOps
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.
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a Flask web application that serves out Machine Learning predictions.
Duration
5 Months
Institution
Duke University
Format
Online
Eligibility Criteria
school
Academic Foundation
A recognized Bachelor’s degree or high school equivalent required for admission into Duke University.
language
Language Proficiency
English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.
Detailed Fees Breakdown
Base Tuition Fee
$114
Total Est. Investment
$114
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the Cloud Machine Learning Engineering and MLOps program at Duke University are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.