Machine Learning in the Enterprise
Machine Learning in the Enterprise
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
Course Info
Objectives
- Describe data management, governance, and preprocessing options.
- Identify when to use Vertex AutoML, BigQuery ML, and custom training.
- Implement Vertex Vizier Hyperparameter Tuning.
- Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI.
Prerequisites
Some familiarity with basic machine learning concepts
Basic proficiency with a scripting language; Python preferred
Audience
- Data Analysts
- Data Engineers
- Data Scientists
- ML Engineers
- ML Software Engineers
Available languages
English, español (Latinoamérica), 日本語, français, 한국어, português (Brasil), and italiano
What do I do when I finish this course?
After finishing this course, you can explore additional content in your learning path or browse the catalog.
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Interested in taking this course with one of our authorized on-demand partners?
Explore Google Cloud content on Coursera and Pluralsight.
Prefer learning with an instructor?
View the public classroom schedule here.
Can I take this course for free?
When you enroll into most courses, you will be able to consume course materials like videos and documents for free. If a course consists of labs, you will need to purchase an individual subscription or credits to be able consume the labs. Labs can also be unlocked by any campaigns you participate in. All required activities in a course must be completed to be awarded the completion badge.