Cloud Machine Learning Engineering and MLOps

Duke University via Coursera

Go to Course: https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke

Introduction

### Course Review: Cloud Machine Learning Engineering and MLOps **Course Overview:** The "Cloud Machine Learning Engineering and MLOps" course is the fourth installment in the Building Cloud Computing Solutions at Scale Specialization on Coursera. This course serves as a pivotal bridge between foundational cloud computing and data engineering concepts and real-world applications of machine learning. It is designed for learners who wish to deepen their understanding of practical machine learning in the cloud and implement best practices in software development and machine learning engineering. **What You Will Learn:** 1. **Getting Started with Machine Learning Engineering:** In this introductory week, learners will explore the methodologies behind machine learning engineering. The focus will be on developing machine learning applications while adhering to software development best practices. By the end of the week, students will have a solid foundation in combining traditional software engineering approaches with machine learning processes. 2. **Using AutoML:** The second week dives into AutoML—a game-changer for those looking to build machine learning solutions without extensive coding. Participants will explore platforms such as Ludwig, Google AutoML, Apple Create ML, and Azure Machine Learning Studio. The practical aspect allows learners to leverage both open-source tools and cloud-based AutoML technologies, facilitating a hands-on approach to creating efficient machine learning models. 3. **Emerging Topics in Machine Learning:** The final week focuses on MLOps—strategies and best practices for deploying and managing machine learning models in production. Besides the essentials of MLOps, the course covers edge machine learning and the utilization of AI APIs. Students will have the opportunity to create a low-code or no-code cloud solution designed for either Natural Language Processing or Computer Vision, solidifying their knowledge with practical applications. **Course Structure & Delivery:** The course is well-structured, combining theoretical knowledge with practical applications. Each week comprises video lectures, readings, and hands-on projects that encourage learners to apply what they have learned in real-world scenarios. The assessments are designed to challenge students while providing valuable feedback and reinforcing their learning journey. **Why You Should Take This Course:** 1. **Practical Application:** The course emphasizes real-world application, providing learners with the skills needed to tackle actual machine learning projects. This hands-on approach is essential for anyone looking to enter the field or enhance their current skills. 2. **Cutting-Edge Tools:** By exploring widely used AutoML platforms, students will gain proficiency in tools that are increasingly relevant in the industry. Understanding these tools can significantly streamline the machine learning development process, making this course a great investment for future career opportunities. 3. **Comprehensive Curriculum:** The course's curriculum is well-rounded, covering vital topics from the foundational aspects of machine learning engineering to the latest trends in MLOps—a critical component of deploying AI projects successfully. 4. **Industry-Relevant Skills:** As businesses increasingly adopt machine learning, understanding the MLOps framework will put participants at the forefront of the industry. The knowledge gained in this course is immediately applicable, making it an excellent choice for professionals looking to stay relevant in their field. **Conclusion:** The "Cloud Machine Learning Engineering and MLOps" course on Coursera is a highly recommended educational resource for anyone interested in machine learning within a cloud framework. Whether you are a student, a recent graduate, or a professional looking to upskill, this course prepares you for practical challenges in the field by giving you the tools and knowledge necessary to succeed. With its comprehensive syllabus and hands-on approach, it stands out as an invaluable asset for your learning journey in machine learning and cloud technologies.

Syllabus

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.

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.

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.