Vertex AI: Google Cloud’s unified AI platform

Vertex AI: Google Cloud’s unified AI platform

Vertex AI is Google Cloud’s unified artificial intelligence (AI) and machine learning (ML) platform. It aims to provide businesses and developers with a comprehensive set of tools and services to build, deploy, manage, and scale ML models and AI applications, all within a single interface and infrastructure. From accessing Google’s pre-trained models (like Gemini or Gemma) to building custom models, and managing the entire ML lifecycle (MLOps), Vertex AI positions itself as an end-to-end solution for enterprise AI.

Key components and features

The strength of Vertex AI lies in the integration of numerous Google Cloud AI/ML tools and services:

• Model Garden: A catalog of pre-trained models from Google (e.g., Gemini Flash, Pro, Ultra), third-party partners, and popular open source AI models (e.g., Llama, Mistral), ready to be used or fine-tuned.
• Generative AI Studio: An interface (similar to Google AI Studio: how-to guide but integrated into Google Cloud) to quickly prototype and customize generative models (text, image, code) without writing code.
• Custom Training: Ability to train your own ML models (TensorFlow, PyTorch, scikit-learn, etc.) on Google Cloud infrastructure, leveraging the power of GPUs and TPUs.
• Vertex AI Notebooks: JupyterLab-based development environments, pre-configured with common AI/ML libraries, for data exploration and model development.
• Vertex AI Pipelines: Tools to orchestrate and automate end-to-end ML workflows (data preparation, training, evaluation, deployment), facilitating MLOps practices.
• Vertex AI Prediction: Services to easily deploy models (pre-trained or custom) and obtain predictions via scalable APIs.
• Feature Store, Model Registry, Monitoring: Tools for managing features, registering and versioning models, and monitoring their performance in production.

The goal is to simplify and accelerate the entire AI/ML development lifecycle.

Benefits for businesses

For companies looking to integrate AI into their operations or products, Vertex AI offers several advantages:

• Unified Platform: Access all necessary tools from a single interface, reducing complexity and improving the productivity of data science and ML teams.
• Access to Cutting-Edge Models: Benefit from Google’s latest models (Gemini) and a wide selection of other models via the Model Garden.
• Scalability and Performance: Leverage the robust and scalable infrastructure of Google Cloud for training and deployment.
• Integrated MLOps: Facilitate model productionization, management, and monitoring with built-in MLOps tools.
• Flexibility: Choose between using pre-trained models, fine-tuning, or training fully custom models.
• Google Cloud Integration: Integrates natively with other Google Cloud services (BigQuery, Cloud Storage, Pub/Sub, etc.).

Vertex AI aims to make enterprise-grade AI more accessible and manageable.

Positioning and competition

Vertex AI is Google Cloud’s answer to similar AI/ML platforms offered by other major cloud providers: Amazon SageMaker (AWS) and Azure Machine Learning (Microsoft). Each of these platforms offers a comparable set of features for the ML lifecycle, but with different specifics, integrations, and pre-trained models. Google often highlights the power of its own models (Gemini, TPUs) and deep integration with its data services (BigQuery). The choice between these platforms will often depend on the company’s existing cloud ecosystem, team skills, specific model requirements, and cost considerations. Vertex AI also needs to consider the rise of more specialized or open-source platforms in certain areas.

Brandeploy and AI generated via Vertex AI

Companies can use Vertex AI to develop and deploy generative AI applications that create brand content (personalized product descriptions, marketing emails, scripts for Google Vids, etc.). In this context, Brandeploy plays its role as the brand governance platform. Models deployed on Vertex AI (whether fine-tuned Google models or custom models) must be “informed” by brand guidelines. This can be done via specific prompts, or by using RAG techniques (LLMs and RAG technique) where the model accesses a validated brand knowledge base managed in Brandeploy. Subsequently, content generated by Vertex AI applications should ideally go through a validation workflow in Brandeploy before use. Brandeploy centralizes the rules, assets, and validation processes to ensure that all AI-generated communication, even via a powerful platform like Vertex AI, is consistent, compliant, and aligned with the brand image.

Vertex AI offers a powerful platform to build and deploy enterprise AI. How do you ensure the generated content respects your brand?

Brandeploy provides the governance framework to manage and validate content from your AI applications on Vertex AI.

Integrate AI power and brand control: request a demo.