Vertex AI: AutoML vs. Custom Model Training

Vertex AI provides two primary methods for training and deploying machine learning models: AutoML and custom training. This guide offers an overview of both approaches, highlighting their strengths and use cases. Whether you're a beginner or an experienced data scientist, Vertex AI offers tools to streamline your ML workflow.

AutoML, or Automated Machine Learning, simplifies the process of creating and training ML models. It requires minimal technical expertise and effort, allowing users to build models without writing code. AutoML uses your training data to learn how to make predictions on new, unseen data. It's an ideal solution for those who want to quickly deploy models without deep technical knowledge.

AutoML can be used to analyze image data for various tasks, including image classification and object detection. Image classification models categorize images, while object detection models identify and locate objects within images. Vertex AI supports both online and batch predictions for image-based models, catering to different application needs. Online prediction is suitable for real-time applications, while batch prediction is efficient for processing large datasets.

Vertex AI enables you to perform machine learning with tabular data through a streamlined process. You can create binary classification models (predicting one of two outcomes), multi-class classification models (predicting a category from multiple options), regression models (predicting continuous values), and forecasting models (predicting a series of values). These models are useful for various applications, such as predicting customer behavior or forecasting product demand.

Please note that as of September 15, 2024, customization for text classification, entity extraction, and sentiment analysis using Vertex AI AutoML models is being deprecated in favor of Vertex AI Gemini. While existing AutoML Text models will continue to function until June 15, 2025, new training or updates will not be supported. AutoML for text data allows for tasks like classifying text, extracting entities, and analyzing sentiment. Consider migrating to Vertex AI Gemini for enhanced capabilities.

AutoML can analyze video data for action recognition, video classification, and object tracking. Action recognition models identify actions within videos, while classification models categorize video content. Object tracking models detect and track objects throughout the video. These capabilities are valuable for applications like sports analytics and video surveillance.

If AutoML doesn't meet your specific needs, Vertex AI allows you to create custom training applications. This approach provides greater flexibility, allowing you to use any machine learning framework and configure the computing resources, including virtual machine types, GPUs, and TPUs. Custom training is ideal for complex models and specialized requirements.

Deciding between AutoML and custom training depends on your project's requirements and your level of technical expertise. AutoML is suitable for quick deployments and users with limited coding experience. Custom training offers more control and flexibility for complex projects and experienced data scientists. Consider the complexity of your model, the need for customization, and your available resources when making your decision. Vertex AI provides comprehensive documentation and tutorials to guide you through both approaches.