Serving open models on Vertex AI: The comprehensive developer's guide

Key Concepts

• Vertex AI Model as a Service (MaaS): Fully managed, serverless APIs for pre-existing models, prioritizing simplicity.
• Model Garden Self-Deploy Models: Deployment of curated open models with user-selected hardware for performance/cost control.
• VLM (Vertex AI Language Model): Backend for optimized containers, offering good performance without custom container builds.
• Custom Containers: Full control over model packaging, frameworks, and logic, enabling maximum flexibility.
• Decision Framework: A control vs. simplicity matrix to guide the selection of the optimal serving strategy.
• GPUs & TPUs: Hardware accelerators for model serving, impacting performance and cost.

Introduction to Serving Open Models on Vertex AI

This video introduces a developer’s guide to serving open models on Vertex AI, focusing on providing a decision framework to navigate the available serving options. The presenter, Ivan Ardini, outlines a series of videos that will cover a complete roadmap with practical code for each serving option, ranging from simple serverless APIs to high-performance custom containers. The core message is to choose the serving strategy that best aligns with a project’s specific needs for control versus simplicity.

The Control vs. Simplicity Decision Framework

The central theme of the video is a decision tree based on the trade-off between control and simplicity. This framework will serve as the guiding principle throughout the series. The presenter emphasizes that the “goal isn’t to find the single best way, but the best way for your project’s needs.”

Serving Options: A Detailed Breakdown

The video details four primary serving options, categorized by their position on the control/simplicity spectrum:

1. Fully Managed Path: Vertex AI Model as a Service (MaaS)

• Description: This option provides popular models as serverless, pay-as-you-go APIs. Users simply select a model, enable the API, and receive an endpoint for immediate use.
• Benefits: Maximum simplicity, rapid deployment, and speed to value. Ideal for prototyping or when infrastructure management is undesirable.
• Trade-offs: Limited control over the underlying infrastructure and model configuration.
• Future Coverage: The first hands-on video in the series will focus on using Model as a Service.

2. Single-Click Deployment Path: Model Garden Self-Deploy Models

• Description: This path allows users to deploy curated open models from the Model Garden, with the key distinction being the ability to choose the underlying hardware.
• Benefits: A balance between ease of use and flexibility, offering direct control over performance and cost.
• Trade-offs: Less control than custom containers, but more than MaaS.
• Future Coverage: A dedicated episode will demonstrate how to configure and deploy these models.

3. Container-Based Serving: Pre-Built Optimized Containers

• Description: Utilizing pre-built containers leveraging backends like VLM (Vertex AI Language Model) or SGLang.
• Benefits: Good performance without the complexity of building containers from scratch.
• Trade-offs: Limited customization options, constrained by the parameters exposed by the container.
• Technical Term: VLM (Vertex AI Language Model) – A backend designed to accelerate language model serving. SGLang - Not explicitly defined in the video, but implied as another backend option for optimized containers.

4. Container-Based Serving: Custom Containers

• Description: Packaging any model with any framework and custom logic into a user-defined container.
• Benefits: Total control over the serving environment, enabling fine-tuning and specialized configurations.
• Trade-offs: Requires significant development effort to build and maintain the container.
• Future Coverage: Subsequent videos will cover building custom containers for both GPUs and TPUs.
• Technical Terms: GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) – Hardware accelerators used to speed up model inference.

Logical Connections and Series Progression

The video establishes a clear progression for the series. It begins with the simplest option (MaaS) and gradually moves towards more complex and customizable solutions (custom containers). Each option builds upon the previous one, providing a comprehensive understanding of the serving landscape on Vertex AI. The presenter explicitly states that the next episode will focus on Model as a Service, providing a practical, step-by-step guide.

Notable Quote

“Remember, the goal isn’t to find the single best way, but the best way for your project’s needs.” – Ivan Ardini. This quote encapsulates the core philosophy of the series, emphasizing the importance of tailoring the serving strategy to specific project requirements.

Conclusion

The video effectively introduces the challenges of serving open models and presents a clear decision framework for navigating the various options available on Vertex AI. By outlining the trade-offs between control and simplicity, and detailing the four primary serving paths, the presenter provides viewers with a valuable roadmap for deploying their models efficiently and effectively. The series promises to deliver practical, hands-on guidance, empowering developers to confidently choose and implement the optimal serving strategy for their projects.