6 Types of AI Workloads, Challenges and Critical Best Practices

What Are AI Workloads?

AI workloads are tasks performed by artificial intelligence systems, which typically involve processing large amounts of data and performing complex computations. Examples of AI workloads are data preparation and pre-processing, traditional machine learning models, deep learning models, natural language processing (NLP), generative AI, and computer vision.

Unlike traditional computing tasks, AI workloads demand high levels of computational power and efficiency to handle the iterative processes of learning and adaptation in AI algorithms. These tasks vary widely depending on the application, from simple predictive analytics models to large language models with hundreds of billions of parameters.

AI workloads often rely on specialized hardware and software environments optimized for parallel processing and high-speed data analytics. Managing these workloads involves considerations around data handling, computational resources, and algorithm optimization to achieve desired outcomes.

This is part of a series of articles about data lake

In this article:

• Types of AI Workloads
• Benefits of AI Workloads
• Challenges of Implementing AI Workloads
• Best Practices to Optimize AI Workloads

Types of AI Workloads

Here are some of the main workloads associated with AI.

1. Data Processing Workloads

Data processing workloads in AI involve handling, cleaning, and preparing data for further analysis or model training. This step is crucial as the quality and format of the data directly impact the performance of AI models. 

These workloads are characterized by tasks such as extracting data from various sources, transforming it into a consistent format, and loading it into a system where it can be accessed and used by AI algorithms (ETL processes). They may include more complex operations like feature extraction, where specific attributes of the data are identified and extracted as inputs.

2. Machine Learning Workloads

These workloads cover the development, training, and deployment of algorithms capable of learning from and making predictions on data. They require iterative processing over large datasets to adjust model parameters and improve accuracy. 

The training phase is particularly resource-intensive, often necessitating parallel computing environments and specialized hardware like GPUs or TPUs to speed up computations.

Once trained, these models are deployed to perform inference tasks—making predictions based on new data inputs. 

3. Deep Learning Workloads 

Deep learning workloads focus on training and deploying neural networks, a subset of machine learning that mimics the human brain’s structure. They are characterized by their depth, involving multiple layers of artificial neurons that process input data through a hierarchy of increasing complexity and abstraction. 

Deep learning is particularly effective for tasks involving image recognition, speech recognition, and natural language processing, but requires substantial computational resources to manage the vast amounts of data and complex model architectures. High-performance GPUs or other specialized hardware accelerators are often needed to perform parallel computations.  

4. Natural Language Processing (NLP)

NLP workloads involve algorithms that enable machines to understand, interpret, and generate human language. This includes tasks like sentiment analysis, language translation, and speech recognition.

NLP systems require the ability to process and analyze large volumes of text data, understanding context, grammar, and semantics to accurately interpret or produce human-like responses. To effectively manage NLP workloads, it’s crucial to have computational resources capable of handling complex linguistic models and the nuances of human language.  

5. Generative AI

Generative AI workloads involve creating new content, such as text, images, and videos, using advanced machine learning models. Large Language Models (LLMs) generate human-like text by predicting the next word in a sequence based on the input provided. These models are trained on vast datasets and can produce coherent, contextually relevant text, making them useful for applications like chatbots, content creation, and automated reporting.

In addition to LLMs, diffusion models are the state of the art method for generating high-quality images and videos. These models iteratively refine random noise into coherent visual content by reversing a diffusion process. This approach is effective in generating detailed and diverse images and videos, useful in fields like entertainment, marketing, and virtual reality. The computational demands of training and running diffusion models are significant, often requiring extensive GPU resources and optimized data pipelines.

6. Computer Vision

Computer vision enables machines to interpret and make decisions based on visual data, mimicking human visual understanding. This field involves tasks such as image classification, object detection, and facial recognition. Modern computer vision algorithms are based on deep learning architectures, most notably Convolutional Neural Networks. Newer approaches to computer vision leverage transformers and multi-modal large language models.

Managing computer vision workloads requires powerful computational resources to process and analyze high volumes of image or video data in real time. This demands high-performance GPUs for intensive computations and optimized algorithms that can efficiently process visual information with high accuracy.

Benefits of AI Workloads 

AI workloads offer several advantages to modern organizations:

• Enhanced efficiency and automation: This frees up resources, allowing organizations to allocate human talent to more complex and strategic initiatives. For example, in manufacturing, AI-driven predictive maintenance can analyze equipment data in real time to predict failures before they occur, reducing downtime and maintenance costs.
• Enhanced decision making: Data analytics and machine learning can provide insights that inform strategic decisions. By processing and analyzing vast amounts of data, AI systems can identify trends, predict outcomes, and recommend actions that are not immediately obvious to human analysts. This capability is useful in areas like financial services, where AI can assess market conditions and investment opportunities.
• Driving innovation: By applying advanced AI algorithms, organizations can solve complex problems, develop new products, and enhance services. For example, in healthcare, AI-driven diagnostic tools analyze medical images more accurately than human professionals, enabling earlier disease detection and personalized treatment.

Challenges of Implementing AI Workloads 

However, there are also some challenges involved in implementing AI workloads:

• Computational resource requirements: AI workloads require substantial resources due to their complex algorithms and data processing needs. Training models, especially in deep learning, require GPUs or TPUs that can handle parallel computations. Inference tasks demand rapid data processing to deliver real-time responses, while the scale of training data requires high-capacity RAM.  
• Scalability: As AI applications grow in complexity and data volumes increase, the underlying infrastructure must be capable of scaling up or out. This includes storage and networking capabilities to support larger datasets and more intensive processing requirements. 
• Data privacy and security: Ensuring the confidentiality, integrity, and availability of data is crucial, especially when processing personal or proprietary information. This requires strong encryption methods, secure data storage solutions, and stringent access controls to prevent unauthorized access or breaches that could lead to data leaks or misuse.
• Maintenance: Ensuring that AI models stay accurate over time necessitates regular retraining with new data, which can be resource-intensive. Additionally, software dependencies must be managed carefully to avoid conflicts or vulnerabilities that could compromise system integrity.  
• Ethical concerns: Implementing AI workloads also raises ethical considerations. Issues such as algorithmic bias, transparency, and accountability must be addressed to ensure fair and ethical use of AI technologies. In addition, generative AI technology can generate harmful outputs or be used by threat actors, making it necessary to put safety measures in place and carefully control their usage. 

Best Practices to Optimize AI Workloads

Here are some of the ways that organizations can improve their AI workloads.

1. Adopting High-Performance Computing Systems 

HPC systems accelerate AI workloads, particularly in tasks that require intensive computations like model training and real-time data analysis. The parallel processing capabilities of HPC environments can reduce the time it takes to train complex models, making iterative development and refinement feasible. They can handle large datasets efficiently, enabling faster data processing and analysis. 

Integrating specialized hardware such as GPUs and TPUs into HPC infrastructures further enhances their capability to support AI workloads. These components perform the parallel computations needed for machine learning and deep learning algorithms, offering improved speed compared to traditional CPUs. This allows researchers and developers to experiment with larger models and more complex simulations. 

2. Leveraging Parallelization and Distributed Computing 

Parallelization in AI workloads involves breaking down complex tasks into smaller, manageable parts that can be processed simultaneously across multiple processors. This approach maximizes the use of available computational resources, speeding up data processing and model training times. Distributed computing extends this concept by spreading tasks across a network of interconnected computers, allowing for even greater scalability and efficiency. 

By leveraging parallelization and distributed computing, AI applications can handle larger datasets and more complex algorithms without being bottlenecked by hardware limitations.

Frameworks such as TensorFlow and Apache Spark provide tools and libraries to distribute tasks across multiple CPUs or GPUs, automating much of the complexity involved in managing distributed systems.  

3. Use Hardware Accelerators 

Specialized processors, such as GPUs, FPGAs, and ASICs, can  enhance the performance and efficiency of AI workloads. By offloading specific computational tasks from general-purpose CPUs to these accelerators, significant speedups can be achieved in processes like model training and inference. This is particularly relevant in deep learning and other complex AI algorithms requiring high levels of parallel processing power. 

Hardware acceleration also reduces energy consumption, making AI applications more sustainable and cost-effective. However, integrating hardware accelerators into AI infrastructure requires careful planning around compatibility and optimization. This includes selecting the right type of accelerator for the workload and type of algorithm.

4. Optimize the Networking Infrastructure 

High-speed networking solutions, such as InfiniBand and Ethernet with RDMA support, provide the low-latency and high-bandwidth communication required for efficiently transferring data between nodes. This enables faster data synchronization across the network, supporting parallel processing tasks and reducing overall computation times in distributed AI systems.

Deploying advanced networking technologies also enables more effective scaling of AI applications, ensuring that network performance can keep pace with increases in computational power and data volume. Implementing network virtualization and software-defined networking (SDN) can further enhance flexibility and manageability, enabling dynamic adjustment of network resources to meet the changing demands of AI workloads. 

5. Use Elastic Object Storage

Elastic object storage solutions, such as Amazon S3 in the cloud or Cloudian AI data lake storage software on-premises, offer scalable and cost-effective ways to manage the vast amounts of data involved in AI workloads. These systems provide high durability and availability, ensuring that data is always accessible when needed for processing or model training. By automatically scaling storage capacity based on demand, elastic object storage eliminates the need for over-provisioning and reduces costs associated with unused storage space.

In addition, these storage solutions support a variety of data access protocols and integrate seamlessly with AI frameworks and tools. This facilitates efficient data ingestion, retrieval, and processing, which is essential for maintaining the performance of AI applications. The use of elastic object storage also simplifies data management by enabling version control and lifecycle policies, helping organizations maintain data integrity and compliance with regulatory requirements.

AI-Optimized Storage with Cloudian AI Data Lakes

Cloudian HyperStore stands out as a storage solution specifically tailored for AI systems, offering scalable, cost-effective, and resilient object storage that meets the unique requirements of AI and ML workloads. A Cloudian AI data lake provides a solid foundation for both stream and batch AI pipelines, ensuring efficient management and processing of large volumes of unstructured data. With options for geo-distribution, organizations can deploy Cloudian software as needed, choosing between all flash and HDD-based configurations to match the performance demands of their specific workload.

The platform’s compatibility with popular ML frameworks such as TensorFlow, PyTorch, and Spark ML streamlines the AI workload optimization process. These frameworks are optimized for parallel training directly from object storage, enhancing performance and compatibility. 

Cloudian HyperStore simplifies data management with features like rich object metadata, versioning, and tags, and fosters collaboration through multi-tenancy, accelerating AI workflows. Moreover, its support for NVIDIA Triton and compatibility with multiple types of stores—feature stores, vector databases, and model stores—empowers organizations to manage, search, and utilize their data effectively, ensuring a robust and efficient AI data infrastructure.

Learn more about Cloudian HyperStore for AI Workloads