Understanding AI/ML Workloads: Types, Benefits, and Challenges

Artificial intelligence (AI) and machine learning (ML) have evolved quickly from one-off experimental projects to mission-critical functions across industries. From powering recommendation engines and fraud detection to enabling predictive maintenance and autonomous systems, AI/ML workloads are reshaping how organizations operate and innovate. But behind the scenes, these workloads require significant compute power, scalable storage, and agile infrastructure—turning today’s IT environments into complex ecosystems of data pipelines, training loops, and real-time inferencing. 

In this article, we’ll break down the types of AI/ML workloads that organizations are running today, explore the benefits they deliver, and examine the key challenges IT teams face in supporting them. Whether you’re planning your first AI deployment or scaling up existing systems, understanding how AI workloads function is essential to building a modern, resilient, and high-performing IT strategy. 

What are AI workloads? 

AI workloads are made up of a variety of applications and computer processes, as well as compute, storage, and networking resources dedicated to running operations for AI and ML systems. They are often the collection of systems and high-performance resources that develop, train, and run AI and ML models. AI workloads are compute-intensive—they’re the powerhouses that make it possible for AI applications to comprehend text, make decisions, solve problems, and innovate.  

Unlike traditional enterprise workloads, which tend to follow predictable, transactional patterns (for example databases, ERP systems), AI workloads are data-heavy and dynamic. They rely on massive datasets and use parallel processing to accelerate complex operations and complete sophisticated tasks. This shift means they demand far more than traditional workloads in terms of compute power, storage performance, and real-time adaptability. 

Key types of AI workloads 

AI workloads span multiple stages of the data lifecycle, each with distinct demands on compute, storage, and data management. Below are the most common types you’ll encounter when building and deploying AI solutions. 

Data preprocessing workloads 

Before any ML model can be trained, raw data must be gathered and prepared for analysis—a step that often consumes the bulk of AI project time. The data preparation process typically involves several key steps, each essential to ensuring that ML models are trained on clean, consistent, and usable data: 

• Collection and ingestion — This step involves gathering data from various sources, such as databases, IoT sensors, APIs, or user interactions, and feeding it into AI pipelines. These pipelines are automated workflows that continuously move data from source systems into processing environments, ensuring that incoming data flows reliably and in the right format for downstream tasks.  

• Cleaning and normalization — Once ingested, the data enters another phase of the AI pipeline where the system removes data inconsistencies, fills in missing values, aligns formats (for example, timestamps or units of measurement), and scales numerical features—all of which are critical for reducing model bias and improving model accuracy and training outcomes.  

• Extract, transform, load (ETL) pipelines —  These pipelines automate the process of pulling data from disparate systems, transforming it into usable formats, and loading it into storage or analysis environments. These ETL workloads often integrate with training pipelines to keep datasets up-to-date and consistent.  

ML workloads 

These workloads form the heart of AI development. They power the creation and refinement of models that learn from data and generate insights or predictions. They require high-performance infrastructure for efficient experimentation, computation, and scalability. Core ML workloads generally fall into two main phases, each demanding significant compute power and playing a critical role in building accurate, reliable AI models: 

• Model development — In this phase, data scientists and ML engineers design the architecture of the model, which entails the selection of algorithms, definition of input features, and setting of hyperparameters (which are configurations that define how an ML model is trained and are set manually before training begins). This phase is highly iterative and involves experimentation with different model types and training configurations to find the best fit for the data and use case.  

• Model training — Once a model is designed, it must be trained on large datasets to learn patterns, relationships, and behaviors. Training is the most computationally intensive phase, especially for deep learning models, and typically requires high-performance graphics processing units (GPUs) or AI accelerators to process millions or billions of parameters. During training, the model’s accuracy is constantly evaluated and adjusted, often over hours or days, depending on the complexity and size of the dataset.  

Data analytics workloads 

AI also drives advanced analytics that enable decision-making and automation across the business. AI-powered data analytics span a range of functions that transform raw data into actionable insights and intelligent automation: 

• Descriptive and predictive analytics — These workloads focus on analyzing historical data to uncover trends and forecast future outcomes. They help you understand what’s happening and what’s likely to happen next. 

• Natural language and visual analysis — AI-powered systems can now interpret unstructured data such as text, audio, images, and video—enabling AI capabilities such as sentiment analysis, facial recognition, and language translation. 

Benefits of AI workloads  

When properly integrated into IT infrastructure, AI workloads deliver value that can transform your organization. From day-to-day efficiencies to long-term innovation, here are some key benefits: 

• Enhanced decision-making — AI enables faster, more accurate decision-making by transforming raw data into actionable insights. Real-time analytics and predictive models can help you anticipate trends, detect anomalies, and respond proactively rather than reactively. AI also helps reduce the potential for human error in making decisions. This accuracy leads to more consistent outcomes, particularly in data-heavy or time-sensitive scenarios such as fraud detection or demand forecasting. 

• Automation — AI workloads excel at automating and streamlining repetitive, time-consuming tasks—freeing up employees to focus on more strategic work. Customer service chatbots, document classification, and comprehensive reporting are just a few examples of AI-driven automation. By integrating automation into workflows, you can enhance productivity across departments, reduce costs, and improve customer satisfaction. 

• Innovation — AI is a powerful engine for innovation, enabling new capabilities and business models that weren’t feasible before. For instance, cutting-edge applications like autonomous vehicles, AI-assisted drug discovery, and personalized medicine are built on advanced AI/ML workloads. By embracing AI before your competitors, you gain a valuable edge in embedding intelligence into your products, services, and operations. 

Challenges and considerations of AI workloads  

As powerful as AI workloads can be, they also come with significant technical, operational, and ethical challenges. It’s important to carefully plan how to support and scale these workloads while minimizing risk and ensuring responsible use. 

Computational requirements 

AI workloads, especially deep learning, require immense computational power. Specialized hardware such as GPUs and tensor processing units (TPUs) are often essential to accelerate training and inference tasks. However, these components can be extremely expensive and require careful system design. 

Also, high energy consumption can boost operational costs. Running large AI models continuously can strain power budgets and infrastructure—especially in data centers not optimized for AI workloads. 

Scalability 

As AI adoption grows, so do the demands on infrastructure. Managing expanding datasets and increasing user demand requires flexible, scalable systems that can grow without bottlenecks or degraded performance. 

The importance of affordable, high-performance AI storage cannot be overstated—you need storage solutions that can keep up with massive throughput, offer low-latency access, and handle ever-growing volumes of data without breaking your budget. 

Security 

AI workloads introduce new attack surfaces that traditional IT security may not fully cover. Risks include data breaches, adversarial attacks (where inputs are manipulated to fool AI models), and even model theft, where proprietary models are copied or reverse-engineered. 

Common mitigation strategies include secure data handling, robust access controls, and encrypted model serving to protect sensitive models and information throughout the pipeline. 

Ethics and compliance 

Building responsible AI means going beyond performance metrics. Bias in training data can lead to skewed or discriminatory outcomes, especially in high-stakes domains like HR, lending and finance, or healthcare. It’s critical to ensure transparency and the ability to explain AI predictions to maintain user trust and make audits easier.  

It’s also essential to adhere to evolving regulatory frameworks such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the European Union Artificial Intelligence Act (EU AI Act), which govern data usage, algorithm accountability, and individual rights. 

Cloud object storage for AI/ML workloads 

To unlock the full potential of AI/ML workloads, you need more than just powerful compute—you need storage that can keep up with the demands of speed, scale, security, and cost. That’s where Wasabi comes in. 

With fast and affordable cloud object storage, we provide a rock-solid foundation for AI workloads—from initial data ingestion and preprocessing to long-term model storage and compliance archiving. Our high-performance infrastructure is built to handle both structured and unstructured datasets, making it ideal for ongoing model training, result comparisons, and regulatory audits. 

Whether you're building your first AI pipeline or scaling a mature deployment, Wasabi helps ensure your data remains accessible, secure, and cost-efficient—at every stage of the journey. 

Learn more about object storage for AI and how we can power your AI/ML workloads today.