On Google Cloud Platform (GCP), TensorFlow and Vertex AI serve different roles in the machine learning lifecycle, each with its specific features and purposes:
### TensorFlow:
1. **Framework**:
– TensorFlow is an open-source machine learning framework developed by Google. It is used to build, train, and deploy machine learning models.
2. **Capabilities**:
– Provides tools for constructing neural networks and other ML models.
– Supports both deep learning and traditional machine learning algorithms.
– Offers high flexibility and control for developers and researchers to customize models.
3. **Usage**:
– Typically used locally or in custom cloud environments where users can install and run TensorFlow libraries.
– Requires more manual setup and configuration when scaling to production or using in distributed environments.
- 4. **Key Components**:
– TensorFlow Core: The main library for building models.
– TensorFlow Extended (TFX): A production-ready mac - -TF Hub
- -TF Statistics
- TF Probability
- TF I/O
1. **Managed Service**:
– Vertex AI is a fully managed ML platform on GCP designed to help developers and data scientists build, deploy, and manage machine learning models more efficiently.
2. **Capabilities**:
– Integrates various Google Cloud services and tools for data preparation, model training, hyperparameter tuning, and model deployment.
– Provides a unified interface for training models with frameworks like TensorFlow, PyTorch, and scikit-learn.
– Offers MLOps features for monitoring, versioning, and automating ML workflows.
3. **Usage**:
– Aimed at simplifying the entire ML lifecycle, from data ingestion to model deployment and monitoring.
– Reduces the need for manual infrastructure management, making it easier to scale ML projects.
4. **Key Components**:
– Vertex AI Workbench: Integrated Jupyter notebooks for data exploration and model development.
– Vertex AI Pipelines: For automating and managing ML workflows.
– Vertex AI Training: Managed infrastructure for training models at scale.
– Vertex AI Prediction: Scalable endpoints for deploying models to make real-time predictions.
– Vertex AI Feature Store: Centralized repository for managing and serving ML features.
### Summary:
– **TensorFlow** is a machine learning framework that provides the building blocks for creating ML models.
– **Vertex AI** is a managed service on GCP that integrates various tools and services to streamline the entire machine learning lifecycle, from data preparation to model deployment and monitoring.
In practice, you might use TensorFlow within Vertex AI to leverage TensorFlow’s flexibility for model building while taking advantage of Vertex AI’s managed infrastructure for training, deploying, and scaling your models.