Technical Theory
Stateful Data Science Agent on Agent Engine | Google Codelabs
Technical Stack
Language
Python
3 / 5
Cloud
Google Cloud
3 / 5
ML Platform
Vertex AI
3 / 5
Orchestration
Agent Engine
4 / 5
Database
Redis
3 / 5
Executive Summary
This codelab guides you through deploying and scaling a stateful data science agent using Agent Engine. It focuses on practical implementation, covering setting up the environment, deploying the agent, and scaling resources to handle increased workloads, making it ideal for data scientists and engineers.
Technical Breakdown
| Category | Technology | Experience | Resources |
|---|---|---|---|
| Language | Python | 3 / 5 |
Documentation |
| Cloud | Google Cloud | 3 / 5 |
Documentation |
| ML Platform | Vertex AI | 3 / 5 |
Documentation |
| Orchestration | Agent Engine | 4 / 5 |
Documentation |
| Database | Redis | 3 / 5 |
Documentation |
Learning Objectives
- Set up the environment for deploying a stateful data science agent.
- Deploy a stateful data science agent using Agent Engine.
- Scale resources for the deployed agent to handle increased workloads.
Key Learning Points
- Understand how to deploy stateful data science agents on Agent Engine.
- Learn to scale resources for data science agents to handle increased workloads.
- Discover the benefits of using Agent Engine for managing data science applications.
Core Skills Gained
- Python
- Google Cloud
- Agent Engine
- Vertex AI
- Redis