Reduce training and development costs
Select an appropriate model or API for each ML task and combine them to create an end-to-end ML development process.
Vertex AI Model Garden offers a vast collection of pre-trained models for tasks such as image classification, object detection, and natural language processing. The models are grouped into the following categories:
Google models like the Gemini family of models and Imagen for image generation.
Open-source models like Gemma and Llama.
Third-party models from partners like Anthropic and Mistral AI.
Google Cloud provides AI and ML APIs that let developers integrate powerful AI capabilities into applications without the need to build models from scratch.
Cloud Vision API lets you derive insights from images. This API is valuable for applications like image analysis, content moderation, and automated data entry.
Cloud Natural Language API lets you analyze text to understand its structure and meaning. This API is useful for tasks like customer feedback analysis, content categorization, and understanding social media trends.
Speech-to-Text API converts audio to text. This API supports a wide range of languages and dialects.
Video Intelligence API analyzes video content to identify objects, scenes, and actions. Use this API for video content analysis, content moderation, and video search.
Document AI API processes documents to extract, classify, and understand data. This API helps you automate document processing workflows.
Dialogflow API enables the creation of conversational interfaces, such as chatbots and voice assistants. You can use this API to create customer service bots and virtual assistants.
Gemini API in Vertex AI provides access to Google's most capable and general-purpose AI model.
Reduce tuning costs
To help reduce the need for extensive data and compute time, fine-tune your pre-trained models on specific datasets. We recommend the following approaches:
Learning transfer: Use the knowledge from a pre-trained model for a new task, instead of starting from scratch. This approach requires less data and compute time, which helps to reduce costs.
Adapter tuning (parameter-efficient tuning): Adapt models to new tasks or domains without full fine-tuning. This approach requires significantly lower computational resources and a smaller dataset.
Supervised fine tuning: Adapt model behavior with a labeled dataset. This approach simplifies the management of the underlying infrastructure and the development effort that's required for a custom training job.
Explore and experiment by using Vertex AI Studio
Vertex AI Studio lets you rapidly test, prototype, and deploy generative AI applications.
Integration with Model Garden: Provides quick access to the latest models and lets you efficiently deploy the models to save time and costs.
Unified access to specialized models: Consolidates access to a wide range of pre-trained models and APIs, including those for chat, text, media, translation, and speech. This unified access can help you reduce the time spent searching for and integrating individual services.
Use managed services to train or serve models
Managed services can help reduce the cost of model training and simplify the infrastructure management, which lets you focus on model development and optimization. This approach can result in significant cost benefits and increased efficiency.
Reduce operational overhead
To reduce the complexity and cost of infrastructure management, use managed services such as the following:
Vertex AI training provides a fully managed environment for training your models at scale. You can choose from various prebuilt containers with popular ML frameworks or use your own custom containers. Google Cloud handles infrastructure provisioning, scaling, and maintenance, so you incur lower operational overhead.
Vertex AI predictions handles infrastructure scaling, load balancing, and request routing. You get high availability and performance without manual intervention.
Ray on Vertex AI provides a fully managed Ray cluster. You can use the cluster to run complex custom AI workloads that perform many computations (hyperparameter tuning, model fine-tuning, distributed model training, and reinforcement learning from human feedback) without the need to manage your own infrastructure.
Use managed services to optimize resource utilization
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