What is AutoML?
AutoML, short for Automated Machine Learning, refers to the process of automating various tasks and processes involved in designing, building, and deploying machine learning models. AutoML aims to make machine learning more accessible to individuals and organizations by reducing the need for extensive manual intervention and expertise in machine learning.
Pros and Cons of AutoML
Pros:
- Saves time and resources by automating tedious and repetitive tasks
- Reduces human errors and biases by using data-driven and objective methods
- Improves performance and quality by exploring a large and diverse search space
- Increases accessibility and scalability by lowering the barriers and costs of machine learning
Cons:
- Lacks transparency and interpretability by hiding the details and logic of the models
- Loses control and customization by relying on predefined and black-box models
- Depends on data quality and availability by requiring sufficient and relevant data
- Raises ethical and social implications by affecting human roles and responsibilities
Vertex AI for AutoML users
The whole machine-learning process, from the preparation of the data through the model deployment, is automated using the AutoML technique. For users with various degrees of expertise and resources, it aims to make machine learning simpler and more efficient. Using diverse methods, such as AutoML or custom code training, and a variety of data types, such as photos, texts, or tables, you may develop and compare models using AutoML. AutoML may also assist you in tracking and explaining the behaviour and performance of your models.
Table of Content
- What is AutoML?
- What is Vertex AI?
- Vertex AI for AutoML users
- What are the benefits of Vertex AI for AutoML users?
- How to use Vertex AI for AutoML?
- Train an AutoML classification model
- Request a prediction from a hosted model
- Conclusion
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