Google Cloud Platform (GCP) provides Vertex AI as their flagship AI product. Vertex AI is an end-to-end platform that allows customers to prepare data, train their models and then deploy them into their other tech infrastructure.

Key Features of Vertex AI

• AutoML: Build high-quality ML models for tasks such as image classification, natural language processing, and working with tabular data, even if you have limited ML expertise.

• Custom Training: Use your own code with popular frameworks (TensorFlow, PyTorch, Scikit-learn) and expand training using GCP's advanced hardware.

• Vertex AI Workbench: Integrated JupyterLab-based environment for development and experimentation.

• Experiment Tracking: Log, compare, and analyze multiple model training runs.

• Model Management: Organize and version your models in a central repository.

• Model Deployment (Endpoints): Easily deploy models as scalable prediction services.

• Pipeline Orchestration: Build and automate reusable ML workflows.

• Explainable AI: Understand your model's decision-making process.

• MLOps Features: Monitor deployed models, detect data drift, and manage production lifecycles.

Explore Vertex AI via the Google Cloud Console. Try out AutoML for fast model training or dive into Vertex AI Workbench for a more interactive development experience.

What are the alternatives ?

Amazon SageMaker

AWS is the most popular cloud platform today and it provides SageMaker, an equivalent of Vertex AI. It has two components. Sagemaker Studio and Sagemaker Autopilot. First is a notebook based approach for data analysis and tuning where as Autopilot allows you to integrate your models into your processes.

AWS services work very seamlessly with SageMaker to help you use AI models everywhere.

Azure Machine Learning

Microsoft's cloud offering has Azure Machine Learning as a product which provides similar services and works seamlessly with rest of the Azure ecosystem.

Vertex AI vs Others

AI infrastructure is still in its early stages, and all cloud services are rapidly improving their offerings. It's hard to declare one service superior to another, but it's generally advisable to stick with your current cloud provider's AI services instead of switching to a new one. If you're a GCP customer, you should definitely consider using Vertex AI over other options.

If you are already with AWS or Azure, then you are better off working with those instead of Vertex AI.

As a developer, it's important to learn the basics instead of committing to just one platform. This involves experimenting with various platforms, understanding their pros and cons, and being ready to use any of them. To keep up with the latest in AI and grasp the fundamentals, following sources like AI Authority can help you stay informed.