Optimizing AI Workloads and Storage: From Data Collection to Deployment
Sponsored by Western Digital.
Artificial Intelligence (AI) has ushered in a new era of technological marvels, ranging from the incredible results of language models to generative AI’s capabilities to create visuals from text.
With its far-reaching possibilities, just about any organization with technical chops can develop (or purchase) its own AI models and train them to analyze and optimize nearly any business process they choose. However, amidst its advancements, and contrary to its perceived complexity, AI workloads are grounded in four fundamental steps—data collection, model creation, training, and deployment.
Besides the computational power of GPUs for crunching data at incredible speeds, one of the key underpinnings that enable the efficient and effective process of AI is data storage.
AI relies heavily on vast data for training, analysis, and real-time decision-making.
Storage systems – whether at the edge, on-premises, or in the cloud – provide the infrastructure to collect, store, manage, and save data and massive AI datasets, ensuring that it is readily accessible for various stages of the AI workflow. Aligning storage technologies with the right phase in the AI workload is crucial to harnessing efficiency and performance, and ultimately insights.
To illustrate, let’s imagine a traditional brick-and-mortar store that seeks to count and categorize the number of customers entering the store.
1. Data Collection:
The initial step involves gathering vast amounts of data – let’s say it’s images from customer movements in a store.
During data collection, storage solutions capture and safeguard the raw data generated from diverse sources like sensors, cameras, and databases. These storage solutions must handle various structured and unstructured data formats, such as images, text, and videos. The ability to efficiently ingest and organize this data is pivotal to the AI process. This raw data requires a local storage server or storage platform, or it is gradually uploaded to the cloud for analysis. In some special cases, a physical data transport appliance or rugged edge server might be needed to capture and move vast amounts of data from the edge to the data center for analysis as it may be too large and/or cost-prohibitive to upload.
Rugged edge solutions can also help ensure seamless data collection in extreme or challenging environments like in a desert or ocean where the internet can be non-existent.
2. Model Creation:
During this phase, with a defined problem in mind, AI experts experiment with different processing steps, refining algorithms to extract desired insights from the data. This is where the magic of AI takes place, fueled by extensive experimentation. While GPUs dominate the modeling and training phase, the storage media choice isn’t necessarily restricted to all-flash arrays. Hard disk drives (HDDs) have a role to play in storing large datasets and snapshots for future retraining.
Machine learning algorithms iterate over these datasets repeatedly to refine and optimize the model. So, while HDDs provide cost-effective bulk storage, Flash proves fast storage ensuring that training and model refinement is performed without bottlenecks.
3. Training:
Training is where the refined AI model is tested and applied to a comprehensive dataset.
Training times can vary greatly; even the most popular language models took almost a year to train, but depending on the problem and training set, some models may only take hours to days to months to train. Regardless, as an AI model grows in complexity, learning as it iteratively adjusts and optimizes, it requires significant GPU power for training, which relies on data stored on some type of media somewhere. While it’s tempting to believe that an all-Flash setup is the sole requirement due to its performance benefits, HDDs are not excluded. Flash and HDDs are complementary. In scenarios where companies amass substantial volumes of data, a hybrid approach is required. As mentioned above, this involves archiving snapshots or older datasets on HDDs, which are available for occasional reprocessing through the training algorithm.
Thus, HDDs find relevance within AI workloads by serving as the repository for bulk data that’s just waiting for the opportune time for reevaluation in subsequent training algorithms.
4. Deployment:
The final step involves deploying the trained algorithm. Once the training process is completed, and companies go to deploy their algorithm. There are various places they can deploy, including edge locations, where real-time data analysis takes place. Some companies leverage the cloud and deploy web-based services.
Others, like the grocery store, could deploy the AI workload on a server in an IT closet in the back of the store. That is where efficient local on-premises edge servers tailored for edge environments can bring the power of the data center to the edge.
Key Questions for AI Workflow and Storage Design
Now that we’ve gone through the four fundamental stages of an AI workflow, here are some important things to consider that will help guide the storage decision-making process to help ensure optimal data storage use and efficiency:
- 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.
- 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.
- Inferencing Infrastructure: Define the hardware planned for edge inferencing. Consider environmental conditions and specific hardware requirements for edge scenarios.
AI is here to stay and is going to revolutionize the way we live – at home and work. The implications on storage are expected to be vast as the role of storage directly influences 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.
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