06
Machine Learning in the Enterprise
06
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, and português (Brasil)
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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.
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View the public classroom schedule here.
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