Cloud Machine Learning Engineering and MLOps
Master machine learning engineering and MLOps with hands-on experience in AutoML, cloud solutions, and emerging technologies for scalable AI systems
Master machine learning engineering and MLOps with hands-on experience in AutoML, cloud solutions, and emerging technologies for scalable AI systems
This comprehensive course explores machine learning engineering principles and MLOps practices for building scalable intelligent systems. Students learn to develop ML applications using software engineering best practices and continuous delivery pipelines. The curriculum covers AutoML technologies, cloud-based solutions, edge machine learning, and AI APIs. Through hands-on experience with tools like Ludwig and Cloud AutoML, participants gain practical skills in implementing and managing ML systems at scale.
Instructors:
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What you'll learn
Implement machine learning engineering best practices
Build and deploy ML applications using continuous delivery
Utilize AutoML for efficient model development
Develop edge machine learning solutions
Integrate AI APIs in applications
Implement MLOps strategies for production systems
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Discussion Prompts, Ungraded Labs
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
The course provides a comprehensive introduction to machine learning engineering and MLOps. Students learn about ML microservices, continuous delivery pipelines, and AutoML solutions. The curriculum covers both open-source and cloud-based tools, edge machine learning implementations, and AI API integration. Practical aspects include working with Flask ML applications, cloud AutoML platforms, and implementing MLOps strategies.
Getting Started with Machine Learning Engineering
Module 1
Using AutoML
Module 2
Emerging Topics in Machine Learning
Module 3
Fee Structure
Instructor
30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.
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