Optimize resource allocation

To achieve cost efficiency for your AI and ML workloads in Google Cloud, you must optimize resource allocation. To help you avoid unnecessary expenses and ensure that your workloads have the resources that they need to perform optimally, align resource allocation with the needs of your workloads.

To optimize the allocation of cloud resources to AI and ML workloads, consider the following recommendations.

Use autoscaling to dynamically adjust resources

Use Google Cloud services that support autoscaling, which automatically adjusts resource allocation to match the current demand. Autoscaling provides the following benefits:

Cost and performance optimization: You avoid paying for idle resources. At the same time, autoscaling ensures that your systems have the necessary resources to perform optimally, even at peak load.

Improved efficiency: You free up your team to focus on other tasks.

Increased agility: You can respond quickly to changing demands and maintain high availability for your applications.

The following table summarizes the techniques that you can use to implement autoscaling for different stages of your AI projects.

Training

Use managed services like Vertex AI or GKE, which offer built-in autoscaling capabilities for training jobs.

Configure autoscaling policies to scale the number of training instances based on metrics like CPU utilization, memory usage, and job queue length.

Use custom scaling metrics to fine-tune autoscaling behavior for your specific workloads.

Inference

Deploy your models on scalable platforms like Vertex AI Prediction, GPUs on GKE, or TPUs on GKE.

Use autoscaling features to adjust the number of replicas based on metrics like request rate, latency, and resource utilization.

Implement load balancing to distribute traffic evenly across replicas and ensure high availability.

Start with small models and datasets

To help reduce costs, test ML hypotheses at a small scale when possible and use an iterative approach. This approach, with smaller models and datasets, provides the following benefits:

Reduced costs from the start: Less compute power, storage, and processing time can result in lower costs during the initial experimentation and development phases.

Faster iteration: Less training time is required, which lets you iterate faster, explore alternative approaches, and identify promising directions more efficiently.

Reduced complexity: Simpler debugging, analysis, and interpretation of results, which leads to faster development cycles.

Efficient resource utilization: Reduced chance of over-provisioning resources. You provision only the resources that are necessary for the current workload.

Consider the following recommendations:

Use sample data first: Train your models on a representative subset of your data. This approach lets you assess the model's performance and identify potential issues without processing the entire dataset.

Experiment by using notebooks: Start with smaller instances and scale as needed. You can use Vertex AI Workbench, a managed Jupyter notebook environment that's well suited for experimentation with different model architectures and datasets.

Start with simpler or pre-trained models: Use Vertex AI Model Garden to discover and explore the pre-trained models. Such models require fewer computational resources. Gradually increase the complexity as needed based on performance requirements.

Use pre-trained models for tasks like image classification and natural language processing. To save on training costs, you can fine-tune the models on smaller datasets initially.

Use BigQuery ML for structured data. BigQuery ML lets you create and deploy models directly within BigQuery. This approach can be cost-effective for initial experimentation, because you can take advantage of the pay-per-query pricing model for BigQuery.

Scale for resource optimization: Use Google Cloud's flexible infrastructure to scale resources as needed. Start with smaller instances and adjust their size or number when necessary.

Discover resource requirements through experimentation

Resource requirements for AI and ML workloads can vary significantly. To optimize resource allocation and costs, you must understand the specific needs of your workloads through systematic experimentation. To identify the most efficient configuration for your models, test different configurations and analyze their performance. Then, based on the requirements, right-size the resources that you used for training and serving.

We recommend the following approach for experimentation:

Start with a baseline: Begin with a baseline configuration based on your initial estimates of the workload requirements. To create a baseline, you can use the cost estimator for new workloads or use an existing billing report. For more information, see Unlock the true cost of enterprise AI on Google Cloud.

Understand your quotas: Before launching extensive experiments, familiarize yourself with your Google Cloud project quotas for the resources and APIs that you plan to use. The quotas determine the range of configurations that you can realistically test. By becoming familiar with quotas, you can work within the available resource limits during the experimentation phase.

Experiment systematically: Adjust parameters like the number of CPUs, amount of memory, number and type of GPUs and TPUs, and storage capacity. Vertex AI training and Vertex AI predictions let you experiment with different machine types and configurations.

Monitor utilization, cost, and performance: Track the resource utilization, cost, and key performance metrics such as training time, inference latency, and model accuracy, for each configuration that you experiment with.

To track resource utilization and performance metrics, you can use the Vertex AI console.

To collect and analyze detailed performance metrics, use Cloud Monitoring.

To view costs, use Cloud Billing reports and Cloud Monitoring dashboards.

To identify performance bottlenecks in your models and optimize resource utilization, use profiling tools like Vertex AI TensorBoard.

Implement the data governance framework

Google Cloud provides the following services and tools to help you implement a robust data governance framework:

Dataplex Universal Catalog is an intelligent data fabric that helps you unify distributed data and automate data governance without the need to consolidate data sets in one place. This helps to reduce the cost to distribute and maintain data, facilitate data discovery, and promote reuse.

To organize data, use Dataplex Universal Catalog abstractions and set up logical data lakes and zones.

To administer access to data lakes and zones, use Google Groups and Dataplex Universal Catalog roles.

To streamline data quality processes, enable auto data quality.

Dataplex Universal Catalog is also a fully managed and scalable metadata management service. The catalog provides a foundation that ensures that data assets are accessible and reusable.

Metadata from the supported Google Cloud sources is automatically ingested into the universal catalog. For data sources outside of Google Cloud, create custom entries.

To improve the discoverability and management of data assets, enrich technical metadata with business metadata by using aspects.

Ensure that data scientists and ML practitioners have sufficient permissions to access Dataplex Universal Catalog and use the search function.

Expand reusability beyond pipelines

Look for opportunities to expand reusability beyond training pipelines. The following are examples of Google Cloud capabilities that let you reuse ML features, datasets, models, and code.

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. It lets you reuse features across different projects and models, which can improve consistency and reduce feature engineering effort. You can store, share, and access features for both online and offline use cases.

Vertex AI datasets enable teams to create and manage datasets centrally, so your organization can maximize reusability and reduce data duplication. Your teams can search and discover the datasets by using Dataplex Universal Catalog.

Vertex AI Model Registry lets you store, manage, and deploy your trained models. Model Registry lets you reuse the models in subsequent pipelines or for online prediction, which helps you take advantage of previous training efforts.

Custom containers let you package your training code and dependencies into containers and store the containers in Artifact Registry. Custom containers let you provide consistent and reproducible training environments across different pipelines and projects.