GCP Vertex AI Pipelines Platform
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A GCP Vertex AI Pipelines Platform is an ML pipelines platform that is a Vertex AI component.
References
2023
- chat
- Q: what is Vertex AI Pipelines
- A: Vertex AI Pipelines is a product offered by Google Cloud that allows users to build, deploy, and manage machine learning pipelines. It is designed to make it easy for data scientists and machine learning engineers to build, test, and deploy machine learning models.
Vertex AI Pipelines provides a number of features and tools to support the machine learning development process, including:
- A visual interface for building machine learning pipelines
- Support for a wide range of machine learning frameworks and libraries
- Tools for managing and organizing machine learning experiments
- Integration with other Google Cloud products, such as BigQuery and Cloud Storage
- A managed service for training and deploying machine learning models
- Vertex AI Pipelines is intended to make it easier for organizations to build and deploy machine learning models at scale. It is particularly useful for organizations that are looking to build and deploy machine learning models in production environments.
2021
- https://cloud.google.com/vertex-ai/docs/pipelines/introduction
- QUOTE: Machine learning operations (MLOps) is the practice of applying DevOps strategies to machine learning (ML) systems. DevOps strategies let you efficiently build and release code changes, and monitor systems to ensure you meet your reliability goals. MLOps extends this practice to help you reduce the amount of time that it takes to reliably go from data ingestion to deploying your model in production, in a way that lets you monitor and understand your ML system.
Vertex AI Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, and storing your workflow's artifacts using Vertex ML Metadata. By storing the artifacts of your ML workflow in Vertex ML Metadata, you can analyze the lineage of your workflow's artifacts — for example, an ML model's lineage may include the training data, hyperparameters, and code that were used to create the model.
- QUOTE: Machine learning operations (MLOps) is the practice of applying DevOps strategies to machine learning (ML) systems. DevOps strategies let you efficiently build and release code changes, and monitor systems to ensure you meet your reliability goals. MLOps extends this practice to help you reduce the amount of time that it takes to reliably go from data ingestion to deploying your model in production, in a way that lets you monitor and understand your ML system.