Artificial Intelligence Workloads and Storage: A Comprehensive Guide

In the realm of technology, Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing industries and redefining our way of life. From the impressive capabilities of language models to the mind-boggling ability of generative AI to conjure visuals from mere text, AI’s potential seems boundless. Organizations with the technical prowess can harness AI’s power by developing or acquiring AI models to optimize various business processes. However, beneath AI’s sophisticated exterior lie four fundamental steps: data collection, model creation, training, and deployment. This comprehensive guide delves into these steps, emphasizing the crucial role of data storage in enabling efficient and effective AI processes.

The Role of Data Storage in AI Workloads

AI’s insatiable appetite for data is undeniable. It thrives on vast amounts of data for training, analysis, and real-time decision-making. Data storage systems, whether residing at the edge, on-premises, or in the boundless expanse of the cloud, provide the infrastructure to collect, store, manage, and safeguard data, including massive AI datasets. Ensuring seamless data accessibility at various stages of the AI workflow is paramount to unlocking AI’s full potential.

Fundamental Steps of an AI Workflow

To illustrate the significance of data storage in AI workloads, let’s consider a traditional brick-and-mortar store seeking to count and categorize customers entering the store.

1. Data Collection

The initial step involves gathering vast amounts of data, such as images of customer movements within the store. Storage solutions capture and safeguard the raw data generated from diverse sources like sensors, cameras, and databases. These solutions must be equipped to handle various structured and unstructured data formats, including images, text, and videos. Efficient ingestion and organization of this data are crucial to the AI process. Temporary storage on a local storage server or platform is often necessary before gradual upload to the cloud for analysis. In extreme or challenging environments, physical data transport appliances or rugged edge servers may be needed to capture and move vast amounts of data to the data center for analysis.

2. Model Creation

With a clearly defined problem in mind, AI experts embark on a journey of experimentation, meticulously crafting processing steps and refining algorithms to extract desired insights from the data. This phase, fueled by extensive experimentation, is where the magic of AI takes place. While GPUs dominate the modeling and training phase, the choice of storage media is not limited to all-flash arrays. Hard disk drives (HDDs) play a vital role in storing large datasets and snapshots for future retraining.

3. Training

The training phase involves testing and applying the refined AI model to a comprehensive dataset. Training times can vary significantly, ranging from hours to months or even a year. As an AI model grows in complexity, learning iteratively, it requires substantial GPU power for training, which relies heavily on stored data. While all-flash setups are tempting due to their alluring performance benefits, HDDs are not excluded. Flash and HDDs complement each other. In scenarios where substantial data volumes are amassed, a hybrid approach is necessary, archiving snapshots or older datasets on HDDs for occasional reprocessing through the training algorithm.

4. Deployment

The final step involves deploying the trained algorithm, unleashing its capabilities to solve real-world problems. Deployment can occur at various locations, including edge locations for real-time data analysis, the cloud for web-based services, or even on-premises servers in specific environments. Efficient local on-premises edge servers tailored for edge environments can bring the immense power of the data center to the edge, enabling real-time decision-making and analysis.

Key Questions for AI Workflow and Storage Design

To ensure optimal data storage use and efficiency, consider the following questions:

1. Data Collection Strategy

Understand the data collection approach—bulk transfer or gradual upload. In some scenarios, physical data transport or a rugged edge server might be necessary.

2. Training Environment

Evaluate whether training should be performed in the cloud, on-premises, or by purchasing pre-trained models. Each option bears its advantages and trade-offs.

3. Inferencing Infrastructure

Define the hardware planned for edge inferencing. Consider environmental conditions and specific hardware requirements for edge scenarios.

Conclusion

AI is here to stay, poised to revolutionize our personal and professional lives. Storage plays a pivotal role in the speed, efficiency, and success of AI processes, making it an indispensable component of modern AI-driven operations. The interplay between data collection, model creation, training, deployment, and cutting-edge storage solutions opens avenues for transformative insights. By aligning storage technologies with each phase, organizations can achieve efficiency and performance in their AI endeavors. In a realm where technology constantly evolves, the right questions and considerations can pave the way for streamlined AI workflow designs that yield impactful results.