This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Instructor: Google Cloud Training
Sponsored by Taipei Medical University [C4CB]
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Containerize ML workflows for reproducibility, reuse, and scalable training and inference on Google Cloud
Efficiently share, discover, and re-use ML features at scale while conducting reproducible ML experiments with Vertex AI Feature Store
Skills you'll gain
- Hybrid Cloud Computing
- Cloud Management
- Google Cloud Platform
- Data Modeling
- Cloud Computing
- Operational Data Store
- Cloud Security
- Cloud Platforms
- Multi-Cloud
- Cloud Infrastructure
- MLOps (Machine Learning Operations)
- Cloud Solutions
- Feature Engineering
- Data Architecture
- Cloud Applications
- Public Cloud
- Database Design
- Applied Machine Learning
- Cloud Services
- Machine Learning
Details to know
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Taught in English
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There are 4 modules in this course
Introduction to the course.
What's included
1 video
Vertex AI and its MLOps capabilities. Main challenges related to data and potential solutions to mitigate them.
What's included
3 videos
Key capabilities of Vertex AI Feature Store
What's included
4 videos1 app item
Summary of the course
What's included
1 video
Instructor
Offered by
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