MLOps with Vertex AI: Manage Features

Google Cloud, located in Toronto, North America, offers you a unique blend of academic and professional opportunities. As a institution founded in 2008, it provides a fresh perspective on education. You will find advantages in its strong focus on computing, data storage, and machine learning, making it an ideal choice for students interested in these fields. Its client base spans various industries, including financial services, healthcare, retail, and education, providing you with a broad range of potential collaborations and career paths.

Specifically, Google Cloud focuses on services like computing, data analytics, and machine learning, with tools such as Google Cloud Platform (GCP) and Google Workspace. You can choose from 723 programs, which cover a broad spectrum of subjects. Key languages and technologies are taught, preparing you for a career in these in-demand fields. For instance, you can develop skills in Gmail, Docs, Drive, and Calendar, making you proficient in Google's suite of productivity tools.

In Toronto, you will find a unique city benefit - a diverse and thriving metropolis with many opportunities for internships, collaborations, and networking. You can take advantage of the city's active startup scene, with many companies using Google Cloud services. On campus, you can engage with a community of 30,000 students, including many international students, creating a dynamic and global learning environment. Opportunities for you include accessing modern labs, and participating in active student clubs, making your time at Google Cloud a rich and rewarding experience.

This program is a non-degree course that teaches MLOps tools and best practices. It's for students who want to learn about deploying, evaluating, and operating production ML systems on Google Cloud. The course lasts several weeks and is taught in English.

In this course, you will learn about MLOps and how to use Vertex AI Feature Store streaming ingestion at the SDK layer. You will also get hands-on practice with deploying, testing, and monitoring production ML systems. The course has 4 modules that cover different aspects of MLOps.

After completing this program, you can work as a Machine Learning Engineer, Data Scientist, or Solutions Architect. You can also work as a Technical Program Manager or a Cloud Engineer. These jobs are available at companies that use Google Cloud and machine learning, such as tech firms and research institutions.