Course Introduction

1. Course Introduction

Welcome to Machine Learning Operations, MLOps, with Vertex AI, Manage Features, the second course of the new series of the Machine Learning Operations topic. Before we begin, let's quickly recap the content from our ML AI curriculum. In the first series of courses, called Machine Learning on Google Cloud, you'll learn about how machine learning on Google Cloud can make tasks better, faster and easier. The second series of courses, Advanced Machine Learning on Google Cloud, focuses on more wide ranging machine learning applications, including computer vision, natural language processing, and recommendation systems. This series of courses is all about Machine Learning Operations and focuses on machine learning models from an operational perspective. This particular course focuses on addressing data-related challenges in MLOps, and how to mitigate them. This content is designed for dedicated or aspiring machine learning data scientists, engineers, and analysts who are interested in learning about machine learning in the cloud and using ML models and Vertex AI. To get the most out of this specialization, it's recommended you have proficiency with Python on topics covered in the Crash Course on Python offered by Google. And prior experience with the foundational machine learning concepts and building machine learning solutions on Google Cloud as covered in the Machine Learning on Google Cloud courses. In the first part of the course, introduction to Vertex AI Feature Store, you will refresh your memory on how Vertex AI can help with MLOps processes, especially on data management and governance. Then you will explore the main challenges related to data and potential solutions to mitigate them. In the second part, Vertex AI Feature Store, an in-depth look, you will discover the key capabilities of Vertex AI Feature Store, which will let you build and deploy machine learning models. Then, you'll learn about the data model and terminology associated with Vertex AI Feature Store resources and components. And finally, you will explore the various storage methods available in Vertex AI Feature Store and learn how to effectively create and manage feature stores on the Vertex AI platform. Through a hands-on lab at the end, you will work on Vertex AI Feature Store streaming ingestion at the SDK layer. More specifically, you will perform the following tasks. Download and prepare data for BigQuery, create a new feature store, create a new entity type, create and write features to the feature store, read features back from the feature store. You also test your knowledge throughout the course with graded assessments. By the end of this course, you'll be able to understand the challenges of managing data in MLOps. Use Vertex AI Feature Store to manage and govern data, automate and streamline the MLOps process from a data perspective. Enroll today to learn about machine learning operations.

2. Let's practice!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.