Managing AI Workloads: Strategies, Tools and Best Practices

AI workloads are the computational tasks and processes required to develop, deploy and run artificial intelligence models in enterprise environments. As organizations increasingly adopt AI to drive business innovation and automation, understanding how to effectively manage these workloads becomes critical for success. This guide explores what AI workloads are, the different types you’ll encounter, key challenges in managing them and best practices for running AI workloads efficiently across cloud and on-premises environments.

Understanding AI workloads

AI workloads differ significantly from traditional computing tasks due to their unique needs for processing power, data handling and specialized hardware. These workloads form the backbone of modern AI-driven applications and services that businesses rely on for competitive advantage.

What are AI workloads?

AI workloads are the compute and data tasks involved in developing and running AI models, including training, inference and data processing. These workloads handle everything from initial data preparation and model training to real-time inference and ongoing optimization. Unlike traditional applications that follow predictable patterns, AI workloads often need massive computational resources and can have highly variable performance characteristics.

The term AI workloads covers several key phases of the machine learning lifecycle. During the training phase, algorithms process large datasets to learn patterns and create models. The inference phase uses these trained models to make predictions or decisions on new data. Data preprocessing workloads clean, transform and prepare raw data for model consumption.

Why are AI workloads important?

AI workloads connect directly to real business outcomes, including scalability, automation and improved performance across operations. They play a crucial role in enterprise AI adoption by making it possible to deploy intelligent systems that can process huge amounts of data, identify patterns and make decisions faster than human capabilities allow.

Organizations that effectively manage AI workloads can automate complex processes, improve customer experiences through personalization and gain competitive advantages through data-driven insights. These workloads support critical business functions, such as fraud detection in financial services, predictive maintenance in manufacturing and personalized recommendations in retail.

Key components of an AI workload

Data ingestion and preprocessing: This involves collecting raw data from various sources and transforming it into formats suitable for AI model training. Data preprocessing workloads handle tasks like cleaning, normalization and feature extraction.

Model training: Training workloads use historical data to teach AI models to recognize patterns and make accurate predictions. This phase usually needs significant computational resources and can take hours, days or even weeks depending on model complexity.

Inference and deployment: Once trained, models need to make predictions on new data. Inference workloads handle real-time or batch prediction requests and often need to meet strict latency requirements.

Monitoring and optimization: Ongoing workloads track model performance, detect drift and trigger retraining when necessary. This helps maintain model accuracy over time as data patterns change.

Types of AI workloads

Different types of AI workloads have distinct characteristics and requirements that influence how organizations should approach their deployment and management strategies.

Training workloads

Training workloads are some of the most computationally intensive tasks in the AI workload spectrum. These processes require high computational demand and often need GPUs or TPUs to complete within reasonable timeframes. Training workloads can be divided into initial training, where models learn from scratch, and transfer learning, where existing models are adapted for new tasks.

Inference workloads

Inference workloads handle the deployment phase, where trained models make predictions on new data. These workloads can be categorized as real-time inference, which needs immediate responses for applications like autonomous vehicles or fraud detection, and batch inference, which processes large volumes of data in scheduled runs.

Real-time vs batch inference is a fundamental design choice that affects infrastructure requirements. Real-time inference usually needs low-latency systems with always-available resources, while batch inference can use more cost-effective approaches that scale resources up and down as needed.

Data-centric workloads

Data preparation, cleaning and labeling for AI/ML make up a significant portion of the total effort in AI projects. These workloads often get less attention than model training but are equally important for getting good results. Data-centric workloads include tasks like data validation, feature engineering and creating training datasets from raw information sources.

Generative AI workloads

LLM fine-tuning and prompt optimization are emerging categories of AI workloads that have gained prominence with the rise of generative AI. These workloads involve adapting pre-trained foundation models for specific use cases or optimizing how applications interact with large language models.

Generative AI workloads often combine elements of training and inference, as fine-tuning requires computational resources similar to training, while serving these models needs strong inference infrastructure. The AI platform considerations for generative workloads include managing large model sizes and handling variable request patterns.

Challenges of managing AI workloads

Organizations could face several significant obstacles when implementing and scaling AI workloads across their infrastructure.

Compute intensity and hardware availability

The challenges with AI workloads begin with the fundamental need for specialized computational resources. Training large models requires access to high-end GPUs or TPUs, which can be expensive and have limited availability. Organizations must balance the cost of purchasing dedicated hardware against the flexibility of cloud-based resources.

Data gravity and latency

Moving large datasets between different computing environments creates significant challenges for AI workloads. Data gravity refers to the tendency for applications and services to be attracted to large datasets, making it more efficient to bring compute to the data rather than moving data to compute resources.

Latency requirements vary dramatically across different AI workload types. Real-time inference applications may need response times measured in milliseconds, while training workloads can tolerate longer processing times.

Cost optimization for cloud AI workloads

Managing costs while keeping up performance is one of the most challenging aspects of AI workload management. Cloud resources for AI can be expensive, particularly when using specialized instances with GPU acceleration. Organizations need strategies for optimizing resource utilization without compromising model performance or availability.

Compliance and security considerations

AI security and compliance requirements add layers of complexity to AI workload management. Organizations must ensure that data handling, model training and inference operations meet regulatory requirements while maintaining security throughout the AI lifecycle.

Best cloud for handling AI workloads: running in the cloud and on-premises

Choosing the right deployment approach for AI workloads requires careful consideration of multiple factors, including performance requirements, cost constraints, security needs and organizational preferences.

What to consider when choosing how to deploy and run AI workloads

Compute power and scalability: Cloud providers offer access to the latest GPU and TPU technologies without needing significant capital investment. Organizations can scale resources up and down based on demand, which is particularly valuable for variable training workloads.

Cost-efficiency and pricing models: Different cloud providers offer various pricing models, including on-demand, reserved instances and spot pricing. Understanding these models and how they apply to different AI workload patterns helps optimize costs while maintaining required performance levels.

Data sovereignty and compliance: Regulations may need data to remain within specific geographic boundaries or organizational control. On-premises deployment gives you maximum control over data location and access, while cloud deployment offers global scalability.

Ecosystem integration: Modern AI workloads benefit from integration with containers, Kubernetes and MLOps tools. The best deployment approach should support these technologies and give you seamless integration with existing development and operations workflows.

Deploying, running and managing AI workloads with SUSE AI

SUSE AI provides organizations with the flexibility to run AI workloads wherever they need them, with enterprise-grade security and management capabilities. SUSE AI supports both cloud and on-premises deployments, giving organizations the freedom to choose the approach that best fits their requirements.

The platform includes comprehensive support for containerized AI workloads, making it easier to get consistent deployment and management across different environments. SUSE AI integrates with Kubernetes orchestration, allowing organizations to leverage the same tools and processes they use for other enterprise applications.

Best practices for running AI workloads

Successful AI workload management requires implementing proven strategies that address the unique challenges of AI computing while maintaining operational efficiency. Here are some good practices to follow:

Optimize resource allocation

Use autoscaling and right-size instances to match resource allocation with actual workload demands. Autoscaling helps manage variable workloads by automatically adjusting compute resources based on current demand. Right-sizing involves selecting instance types that give you the optimal balance of performance and cost for specific workload characteristics.

Use containerized environments for portability

Containerization offers consistent deployment environments across different infrastructure types and cloud providers. Containers package AI applications with their dependencies, making it easier to move workloads between development, testing and production environments.

Set up MLOps pipelines for continuous training and deployment

MLOps pipelines automate the end-to-end process of developing, deploying and keeping AI models. These pipelines handle tasks like data validation, model training, testing and deployment, reducing manual effort and improving consistency.

Monitor for model drift and performance issues

Model monitoring should track both technical performance metrics and business impact measures. Technical metrics include accuracy, latency and resource utilization, while business metrics might include conversion rates, customer satisfaction or operational efficiency.

Early detection of model drift allows organizations to address issues before they significantly impact business operations. Monitoring systems should give you alerts when model performance degrades beyond acceptable thresholds and trigger automated responses when possible.

Real-world use cases of AI workloads

Understanding how different industries apply AI workloads helps illustrate the practical benefits and implementation approaches across various business contexts.

Healthcare: Diagnostic image analysis

Medical imaging is one of the most successful applications of AI workloads in healthcare. These systems process X-rays, MRIs and CT scans to identify potential health issues faster and more accurately than traditional methods.

Finance: Fraud detection and risk assessment

Financial services use AI workloads to analyze transaction patterns and identify potentially fraudulent activities in real-time. These systems process millions of transactions daily, applying machine learning models that have been trained on historical fraud data.

Retail: Personalized recommendations with LLMs

Modern retail organizations use AI workloads to create personalized shopping experiences through recommendation systems powered by large language models. These systems analyze customer behavior, purchase history and product information to suggest relevant items.

SUSE AI gives you the freedom to run AI workloads that work for you

The success of AI initiatives depends on having the right infrastructure, tools and support to manage complex workloads across diverse environments. SUSE AI gives organizations the flexibility to deploy AI workloads where they make the most business sense, whether that’s in public clouds, private data centers or hybrid environments.

Key advantages of SUSE AI include enterprise-grade security that protects sensitive data throughout the AI lifecycle, comprehensive support for both traditional and generative AI workloads and integration with existing enterprise systems and processes.

Organizations that choose SUSE AI benefit from the freedom to run workloads wherever they need them without vendor lock-in or compromising on security and compliance requirements. This flexibility becomes increasingly important as AI strategies evolve and organizations need to adapt their infrastructure to support new use cases and requirements.

Ready to optimize your AI workload management strategy? Explore how SUSE AI can help your organization deploy, manage and scale AI workloads with the security and flexibility that enterprise environments demand.

AI workloads FAQs

What are AI workloads?

AI workloads are the compute and data tasks involved in developing and running AI models, including training, inference and data processing.

What are examples of AI workloads?

Examples include model training, real-time inference, data preprocessing and generative AI tasks such as LLM fine-tuning.

How do AI workloads differ from traditional workloads?

AI workloads require specialized hardware like GPUs, handle larger datasets, need more computational power and have variable performance patterns compared to traditional applications.

Do AI workloads require GPUs?

While GPUs significantly speed up most AI workloads through parallel processing capabilities, some simpler AI tasks can run on CPUs. The choice depends on model complexity and performance requirements.

How do you optimize AI workloads in the cloud?

Use autoscaling, containerization, cost monitoring and MLOps pipelines to make sure you get high performance and efficiency while managing costs through proper instance selection and resource scheduling.

Are AI workloads expensive to run?

Costs vary based on specialized hardware requirements, data storage needs and compute intensity. Proper optimization strategies, including spot instances, autoscaling and efficient resource allocation, can help manage expenses.

What are the best practices for running AI workloads?

Leverage GPUs for training, monitor resource utilization, set up security best practices, automate deployments with Kubernetes and use AI security and compliance frameworks to protect sensitive operations.