provides tools for every step of the machine learning workflow across different stages of machine learning development. It allows for Dataset Preparation (Gathering, Preprocessing and Version Control), Train (AutoML and Custom) and Deploy ML models and AI applications — all of these using the benefits of Google Cloud Introduction to Vertex AI Vertex AI: Introduction
to leverage processes like data ingestion, preprocessing, storage. Also advanced features like data encryption, access control, compliance certification, data governance etc. Pros #8
Vertex AI and select open-source models (some pretrained) and assets Workbench: Jupyter Notebook based development environment, integrates Cloud storage and BigQuery to access and process data faster Pipelines: build and monitor pipelines to helps to automate, monitor and govern ML systems by orchestrating ML workflow in a serverless manner and store workflow’s artifacts using Vertex ML metadata Generative AI Studio: create, experiment with generative aI models. Test and customize Google’s LLMs Data: All data preparation takes place, can label, annotate and do a lot on the data Model Development: We can train ML models using either AutoML or Custom. After training, we can assess the model, optimize and even understand the signals behind the model’s predictions with “Explainable AI” Deploy and Use: Deploy Model to an endpoint to serve for online predictions using the API or the console. Includes all the physical resources and scalable hardware needed to scale the model for lower latency and online predictions. Undeployed model can also be used for Batch predictions using CLI, console UI or the SDK and the APIs. Each model can have multiple endpoints