Why use GKE for AI/ML inference

This document is the first part of a series that's intended for Machine learning (ML) engineers who are new to using GKE, and want to start running inference workloads on GKE as quickly as possible.

In this series, we won't attempt to teach you about all of the available GKE options. Instead, we provide you with the basic information that you need to know to get your workloads on GKE. This series includes the following documents:

As an AI/ML practitioner, you can use Google Kubernetes Engine (GKE) ( GKE) to help you manage your model deployments. GKE is a service that lets you automate the deployment, scaling, and monitoring of your Kubernetes-based inference workloads and the computing infrastructure that your workloads run on. This document covers some of the key benefits of using GKE for model inference, including the following:

Before you begin

Before you read this document, you should be familiar with the following:

Package your models for consistent performance

Consistent and reliable model inference is crucial for your AI application, and GKE can reduce the operational complexity of managing inference at scale. By packaging your trained model, inference server, and all dependencies into a container, you create a standardized, portable artifact that runs consistently on GKE. Containerization helps reduce errors that are caused by mismatched dependencies or inconsistent environments. GKE automatically deploys and manages these containers. It handles tasks like restarting crashing workloads or reprovisioning resources to help stabilize the performance of your AI/ML workloads.

Use GPUs and TPUs to reduce serving latency

Latency is an issue for inference workloads, and some complex models rely on hardware accelerators like GPUs and TPUs to accelerate inference serving. GKE streamlines the process of using GPUs or TPUs for your latency-sensitive inference workloads. You choose the specific hardware configurations that best fit your requirements for performance and cost, and GKE automatically provisions and manages the hardware that you choose. For example, GKE automates NVIDIA driver installation, which is a step that typically requires manual configuration in Kubernetes.

GKE also offers resource management capabilities so that you can more efficiently use TPU and GPU resources for inference serving. For example, you can use GKE to schedule or share GPU or TPU pools across multiple models, or you can use accelerators with Spot VMs to increase cost efficiency for fault-tolerant inference workloads. These capabilities help you maximize your use of accelerator resources while optimizing for both serving latency and cost.

Handle fluctuating traffic patterns automatically

Traffic and load on real-time inference workloads can be unpredictable and dynamic. A spike in demand might lead to increased latency and reduced performance. GKE offers a multi-layered approach to autoscaling so that you can automatically add or remove resources to meet variable inference demand. For example, you can use the Horizontal Pod Autoscaler (HPA) to automatically adjust the number of Pods in a Deployment, or cluster autoscaler to automatically adjust the number of nodes in an existing node pool. By using GKE autoscaling capabilities, you can efficiently match the amount resources that your inference workload needs to the amount of application demand.

Monitor the health and performance of your inference workload

GKE is integrated with the Google Cloud observability suite (Cloud Logging and Cloud Monitoring), and you can use built-in observability features to monitor the health and performance of your inference workloads. These observability features give you insight and visibility into how your workload is performing after you deploy it. For example, you might wonder whether your model is working as expected, or whether your workload meets your requirements for latency and accuracy.

GKE automatically reports infrastructure metrics like CPU, memory, and accelerator utilization. To answer questions about model-specific performance, you can use Google Cloud Managed Service for Prometheus or send custom metrics from your inference application to Cloud Monitoring. For example, you can configure automatic application monitoring and monitor key inference metrics, such as requests per second (RPS); monitor concept drift by analyzing model-specific metrics, such as input data distribution; and debug issues by conducting historical log analysis.

Use GKE for portability and flexibility

By using open standards like containers and open-source technologies like Kubernetes, GKE gives you the freedom to move your inference serving workloads to different locations–and use different resources and tools–as your requirements change. For example, you can develop and test your inference application on GKE, and then deploy that same containerized application to your on-premises environment for production.

In conclusion, you can use GKE to streamline how you move your AI/ML models from development to production. GKE handles many complexities that are involved with infrastructure management, which means that you can focus on running your inference workloads on a performant, scalable, and observable environment. As you go through this series, you will learn how you can use GKE to transform your AI/ML models into powerful, production-ready applications.

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