Vertex AI provides a unified environment for developing, training, and deploying text and image models without needing to manage separate infrastructure for each modality. For text models, developers can use pre-trained language models available in Vertex AI Model Garden, or train custom models using frameworks like TensorFlow or PyTorch. These models can be fine-tuned for specific use cases such as sentiment analysis, summarization, or document classification. For image tasks, developers can either use AutoML Vision (which automates feature extraction and training) or bring their own custom convolutional neural network (CNN) architectures for classification, object detection, or segmentation.
The workflow typically begins by organizing data in Google Cloud Storage. For text models, data might be CSV files containing text and labels; for image models, it might be image folders with class metadata. You then create a Vertex AI dataset, which provides built-in validation, splitting, and labeling tools. After defining a training job, Vertex AI handles distributed training, evaluation, and artifact storage automatically. Developers can view training metrics in Vertex TensorBoard and deploy the resulting model directly to a managed endpoint for real-time or batch predictions.
For projects involving vector embeddings, Vertex AI can generate embeddings from text or images and store them in Milvus for similarity search. This enables advanced applications like semantic image retrieval or document lookup, where the system retrieves visually or contextually similar results. For example, an e-commerce team could use a Vertex AI Vision model to embed product images, store them in Milvus, and serve “find similar items” queries efficiently. This integration makes Vertex AI not just a training and deployment tool, but part of a complete retrieval-aware pipeline that connects multimodal data to intelligent search and recommendation systems.