Many companies are modernizing their data storage infrastructure to capitalize on the opportunities of machine learning (ML) and advanced analytics. However, teams face several unique data management challenges such as the increasing time required for AI training and inference workloads, as well as the cost and scarcity and resources, particularly GPUs.
Storage is a key aspect of AI and a part of its complexity. It provides data for training and stores potentially massive amounts of data collected during inference when AI conclusions are applied to real-world tasks.
What is AI data storage, and how can teams maximize its value for their AI applications? Continue reading to explore the essentials of AI data storage.
What is AI Data Storage?
AI storage is a specialized infrastructure that supports machine learning and artificial intelligence workloads. It provides high-performance, scalable storage choices for managing huge data volumes generated by AI applications, ensuring fast access and processing of the data.
To optimize efficiency and save costs, these storage systems usually feature data reduction technologies such as deduplication, compression, and tiering. Furthermore, AI storage places a heavy emphasis on data security, using access control and encryption to protect sensitive data. Integrating AI frameworks simplifies data access and processing, allowing AI and machine learning applications to be developed.
As AI technology spreads throughout industries, there is an increased demand for storage solutions for AI workloads. Effective AI storage solutions increase the performance of both training and inference activities by allowing for faster data retrieval and processing.
AI Data Storage vs AI Data Warehouse
While both AI data storage and data warehousing are essential for AI/ML, they serve different purposes: AI data storage focuses on handling massive, diverse, and unstructured datasets for AI workloads, whereas data warehousing provides a structured and organized environment for cleansed and processed data, making it perfect for business intelligence (BI) and reporting purposes.
How Does AI Data Storage Work?
Huge parallel processing using GPUs is at the heart of AI infrastructure. Storage, in essence, feeds those GPUs as quickly as possible, enabling the full utilization of these costly hardware products.
Typically, this involves using flash storage for low-latency I/O. The required capacity will vary depending on the size of the workloads and the expected size of the AI processing outputs, but hundreds of terabytes, if not petabytes, are likely.
Adequate throughput is also important because different AI frameworks store data differently, such as PyTorch (a high number of tiny files) and TensorFlow (the reverse). This means that it’s not simply a matter of rapidly getting data to GPUs but also acquiring the correct volume and I/O capability.
Storage suppliers are offering flash-based storage – generally using high-density QLC memory – as viable general-purpose storage, particularly for datasets that were previously considered “secondary,” such as backup data, because consumers may now wish to access them faster using AI.
In any case, storage for AI projects will range from high performance during training and inference to various forms of long-term retention. This is because it’s not always evident at the start of an AI project what data will be relevant.
Key Features of AI Workloads
AI and machine learning rely on training algorithms to spot patterns in data, acquire insight into data, and often trigger responses based on those results. These might be basic, sales data-driven recommendations or complex content produced by large language models (LLMs) in a generative AI application.
AI workloads have three essential phases and deployment types:
- Training
- Inference
- Deployment
AI and machine learning workloads can be trained and deployed in various locations and environments, to continuous application workloads.
Training and operational datasets may contain many small files, such as sensor readings in Internet of Things (IoT) applications, and very large objects, such as image and movie files or discrete batches of scientific data. The AI frameworks used also determine the file size at the time of intake.
Datasets could also be used for primary or secondary data storage, such as sales records or backup data, which is becoming increasingly valued as a source of company information. Data is more likely to be unstructured and in vast amounts than structured and in databases.
AI workloads typically call for massively parallel computing, which employs graphics processing units (GPUs) or other hardware that offloads processing from central processing units (CPUs).
Processing performance must be exceptional to manage AI training and inference in a suitable timeframe and as many iterations as possible to maximize quality.
Infrastructure may also require huge scaling to handle massive training datasets and inference outputs. Other requirements are fast I/O between storage and processing and managing data portability between places for the most effective processing.
AI Data Storage Use Cases
Finance
As AI continues to make headlines in the financial sector, new use cases emerge, driving further adoption by financial institutions. Financial companies use AI data storage to manage massive amounts of transactional data, market trends, and client information. This paves the way for developing AI-powered models for fraud detection, risk assessment, and algorithmic trading, thereby enhancing the efficiency and security of financial operations.
Healthcare
As healthcare organizations struggle to deal with rising volumes of complicated data, AI can deliver far more than traditional storage management tools. AI-enabled storage is the backbone of advanced analytics solutions that analyze data usage patterns, optimize storage resources, automate routine tasks, and eliminate human error, resulting in increased efficiency and cost savings.
In the healthcare industry, AI data storage is critical for storing big datasets created by medical imaging, electronic health records (EHRs), and genetic research. These storage options make it easier to build AI models that can detect diseases, forecast patient outcomes, and tailor treatment strategies to individual needs.
Manufacturing
AI data storage helps with predictive maintenance, quality control, and supply chain efficiency. By storing data from sensors and machines in specialized storage solutions, manufacturers may use AI to detect equipment breakdowns, improve product quality, and optimize manufacturing processes.
Benefits of AI Data Storage
The use of AI data storage solutions provides various advantages:
| Advantage | Description |
|---|---|
| Effective management of massive amounts of data | AI data storage solutions ensure that AI applications can access the data they require for training and inference. |
| Reduction of latency and accelerated data processing | Both are crucial for real-time AI applications. |
| Simplified data workflows and improved operational efficiency | Such tooling allows enterprises to get more out of their data. |
| Scalability | AI data storage solutions have substantial advantages over standard data storage systems, particularly in meeting the special requirements of AI workloads. Compared to standard storage, AI data storage offers higher scalability to support the exponential rise of data generated by AI applications. |
| High performance | AI data storage provides high-performance features, such as quick data retrieval and processing, which are critical for training AI models and performing real-time analytics. |
| Powerful data management | Features such as enhanced indexing and metadata management help to optimize processes and improve operational efficiency. They’re built to interface smoothly with common AI frameworks, ensuring efficient data transmission and interoperability. |
| Robust security measures | Organizations can easily protect sensitive AI data from breaches and unwanted access. |
Challenges in AI Data Storage
AI storage challenges include scalability, performance, and data management. To accommodate expanding AI datasets, storage systems must be scalable. AI tasks require high-performance storage. AI storage implementations must also include data reduction, such as deduplication and compression, to enhance storage efficiency and save money – data security and regulatory compliance present additional challenges.
To fully realize the potential of AI and ML applications, teams must address several issues:
| Challenge | Description |
|---|---|
| Massive data volumes | AI applications create and process large volumes of data. Managing and storing such big amounts can be difficult and necessitates scalable storage solutions. |
| Format diversity | AI data comes in various formats, including structured, unstructured, and semi-structured data. Handling various data types and ensuring interoperability with storage systems is critical. |
| Data velocity | The rapid data generation and processing can burden storage systems. High-velocity data necessitates storage solutions that enable rapid data ingestion and retrieval. |
| Integration | Effective AI operations require seamless integration with AI frameworks and processing units. Ensuring interoperability and smooth data flow between storage and AI systems can be challenging. |
| Compliance | Organizations must guarantee that their data storage systems follow applicable legislation and standards. This encompasses data privacy legislation, industry-specific restrictions, and worldwide standards. |
| Scalability | As AI applications increase, storage solutions must also scale. Planning for future scalability and eliminating bottlenecks is an important consideration. |
| Latency | This is an important consideration for real-time AI applications. Storage systems must provide fast data access and processing to address the needs of time-sensitive applications. |
Best Practices for AI Data Storage
Implement Robust Version Control for Datasets
The machine learning development process is highly iterative, with developers seeking the best-performing model while adjusting hyperparameters, code, and data. It is critical to retain a record of these modifications to track model performance relative to the parameters, saving time spent retraining the model for experimentation.
There’s metadata, which contains information about your data and model, and real data, which you use to train and test your model. Metadata can change without affecting the data, and versioning should link the data to the proper meta.
Ensure Data Consistency and Integrity Across Pipelines
The initial stage in every machine learning pipeline is to collect the data that will be used to train, test, and deploy the models. This phase entails collecting, extracting, and ingesting data from various sources, including databases, APIs, web pages, sensors, and files.
To maintain data consistency and integrity in this step, each source’s and destination’s data schemas and formats must be defined clearly and consistently.
Data cleaning is the next phase in any ML pipeline. It entails finding and resolving issues, including outliers, duplicates, inconsistencies, noise, and errors. To assure data consistency and integrity, use EDA tools to analyze data distribution, variation, and relationships.
The final phase in any ML pipeline is data validation, which ensures that the data fulfills the models’ and stakeholders’ expectations and requirements. As a result, it is critical to create and apply data validation criteria for all data elements and attributes. Additionally, using data validation tools like testers, checkers, and alerts can assist in detecting and reporting mistakes.
Optimize Storage for Scalability and Performance
Scalability is essential for AI systems, so ensuring that there is always enough space to accommodate the constant influx of data that drives AI progress is a critical component of AI storage.
Scalable storage indicates that you are ready to meet and react to new requirements while developing AI applications and systems. It contributes to maintaining an optimal infrastructure for today’s business ecosystem in the face of constant change.
The amount of data that AI systems process necessitates high storage performance. When AI systems have instant access to data, they run more smoothly and efficiently. Furthermore, they become less time-consuming, a significant advantage in the era of self-driving automobiles and automated stock trading, where time is crucial.
AI storage’s speed enables faster training on large, complicated datasets, making models more accurate and trustworthy. It also allows systems to scale up without slowing down.
Automate Data Lifecycle Management and Archiving
AI data lifecycle management is the process of managing AI systems’ data from beginning to end, assuring high-quality data and regulatory compliance. Proper data management is critical because poor data quality, a lack of transparency, and insufficient governance can result in biased models, incorrect forecasts, and non-compliant AI systems.
Enforce Strong Security and Access Controls
Security and privacy must be prioritized when storing data for AI systems. Implementing these practices correctly is critical to creating user confidence and ensuring compliance with global standards such as GDPR and CCPA.
Furthermore, prioritizing security and privacy from the outset will deter hostile actors and secure your AI data from hackers, breaches, and tampering. Moreover, in today’s unpredictable threat landscape, AI storage should be built on the principles of trust, transparency, dependability, resilience, and ethics.
Monitor and Audit Data Usage for Compliance
Adding data governance standards to data pipelines, like finding and masking sensitive attributes, can be useful. Because governance is used at entry, data lakes cannot leak sensitive data.
Doing so within the data fabric framework automates the process. Data fabric, with its unified view of data and support for governance activities, helps to enforce and facilitate adherence to data quality standards, security measures, and regulatory needs. Implementing strong data governance processes is critical for enterprises to make informed decisions while ensuring data assets’ dependability and trustworthiness.
Future Trends in AI Data Storage
The future of AI data storage looks bright as AI-powered solutions become mainstream and we see more solutions based on AI in data engineering and data management. Needless to say, AI will increasingly impact storage technologies, including tasks like tiering, migration, optimization, provisioning, backups, and other forms of data protection, such as failure prediction.
Administrators will use AI for storage optimization, for example, to assess usage trends and system resource utilization. An AI engine can then use this data to offer recommendations on how to distribute resources to avert problems, cut expenses, or both.
Other directions for the future include handling metadata and, specifically, AI metadata management for efficient, scalable, and compliant AI products.
How lakeFS Empowers AI Data Storage
In the world of machine learning, managing the output is just as crucial as controlling the input: the data. This is where version control comes in to ensure high-quality data.
lakeFS, an open-source system, introduces version control to data. It sits on top of an object storage and allows teams to manage their data using Git-like operations (branch, commit, merge, etc.) while handling billions of files and petabytes of data.
One of the most essential features of lakeFS is environment isolation. Using lakeFS, several data practitioners can work on the same data while creating a separate branch for each experiment. Data can be tagged to represent individual experiments, allowing them to be replicated using the same tag.
Once the update has worked for you, you may push it or merge it back into the main branch to make it available to customers. Alternatively, you can undo modifications without going through each file manually, like with S3. You can undo the alteration and revert to the previous good condition.
Conclusion
AI data storage solutions are a key component of the modern AI landscape, which evolves at a staggering speed. We can expect storage solutions to follow the same pace to match the unique demands of AI workflows and compliance requirements proposed by new regulations.
