The long-term success of an AI model is directly correlated to the quality of the data used to power it. Across the AI data pipeline, workloads vary in intensity and data types, and object sizes can fluctuate. Efficiently managing data throughout the AI data pipeline is essential to ensuring AI initiatives remain cost-effective and technically feasible. By choosing the ideal storage at each step, organizations can better position their AI projects for long-term success. 

What is an AI data pipeline? 

An AI data pipeline is the framework through which data moves within a tech stack to support AI-driven use cases. It includes the tools and processes used to extract, transform, and deliver data to train and operate AI models. This pipeline typically includes six phases, each with distinct storage requirements: 

1. Raw data ingest 

2. Data preparation 

3. Training 

4. Fine-tuning 

5. Inferencing/deployment  

6. Archiving 

Each phase involves different data volumes, access patterns, and performance needs. Therefore, it makes sense to use the type of storage that is best suited for each particular phase of the pipeline.   In the next section, we will review different pipeline phases and provide a quick overview of the storage that is best suited for them.  

What type of storage is needed for each phase of an AI data pipeline? 

1. Raw data ingest: Large volumes of data are collected from numerous sources that will feed into and inform the AI workflow. 

• Storage volume: Large 

• Type of storage: Object storage 

• Rationale: Data in this phase can be both semi-structured and structured, including images, videos, sensor outputs, and text, resulting in many petabytes of data. Object storage is ideal here due to its ability to handle unstructured data at scale cost-effectively. It also supports parallel data ingest and integrates well with tools that prep the data for downstream processes. 

2. Data preparation: The raw data is cleaned, labeled, and transformed into formats used for model training. 

• Storage volume: Medium 

• Type of storage: High-performance file storage 

• Rationale: This step requires frequent data reads/writes and low latency, making high-performance file storage optimal. The data volume is usually a subset of the data that was ingested in phase one, but the access patterns are intense, requiring faster input/output (I/O) than object storage typically provides. 

3. Training: Datasets are processed to develop machine learning models that recognize patterns and make predictions. 

• Storage volume: Medium 

• Type of storage: High-performance file or in-memory storage. In-memory storage stores data in the main memory (RAM) of a device to speed up data processing. This technique is a contrast to conventional data storage approaches where data is stored on disk drives.  

• Rationale: Training data is typically a smaller subset of the raw data that must be accessed with high frequency and speed. GPU clusters or accelerators often require fast I/O, so high-performance file storage or in-memory storage solutions (with support for parallel reads) are best.  

4. Fine-tuning: This is the process of adapting a model trained in the previous phase to a specific task or dataset, improving its performance and making it more tailored to a particular use case.  

• Storage volume: Small 

• Type of storage: High-performance file or in-memory storage 

• Rationale: In-Memory/CPU or GPU or high-performance file storage should be used for fine-tuning because it is a resource-intensive process that demands low latency, high throughput, and fast access to data. New input data can be delivered from object storage (raw data) to the fine-tuning storage destination. 

5. Inferencing/deployment: Finalized AI/ML models are integrated into a production environment where they can be accessed and used by applications and end users. 

• Storage volume: Medium 

• Type of storage: In-Memory/CPU or GPU 

• Rationale: In deployment, models are made available for production use, often requiring the highest speed access possible to both the model files and reference data. Storing data In-Memory/CPU or GPU minimizes latency, ensuring fast, real-time predictions and a responsive user experience. 

6. Archiving: Older datasets, models, prompt outputs, and log data are stored for long-term retention, compliance, or future retraining needs. 

• Storage volume: Large 

• Type of storage: Object storage 

• Rationale: Object storage is an ideal fit for archival data, providing long-term, scalable, cost-effective, and secure storage that keeps archived content readily accessible when needed. 

How cloud object storage can improve the economics and scalability of AI workflows  

While high-performance storage is essential for certain phases of the AI data pipeline, such as data preparation, training, and production. Storing all AI workflow data on these premium storage tiers is often unnecessary, expensive, and unsustainable. Cloud object storage offers a scalable, economical alternative that aligns well with the requirements of several stages in the AI data pipeline. 

Object storage is particularly well-suited for large data capacities, both at the raw data ingest and output of archives. This raw data can also be accessed during training and fine-tuning as new input data to train models. These stages typically involve large volumes of data that don’t demand ultra-low latency or constant read/write activity, making cost-efficiency and scalability critical. Object storage also integrates seamlessly with AI infrastructure tools used in these phases, like cloud-based compute services. 

However, not all cloud object storage solutions are created equal. Many providers charge data access fees, such as reads, writes, and egress. This can introduce unpredictable and often significant costs, especially in AI workloads where data may be frequently reused or reprocessed. Wasabi Hot Cloud Storage removes these limitations with predictable pricing with no fees for egress or API requests, making it easier for organizations to manage AI budgets. This is especially critical when dealing with iterative workflows like retraining or real-time inferencing, where frequent data access is unavoidable. 

Additionally, in steps like archiving, organizations may consider options like cold storage. However, this is often an unsuitable choice for AI systems that rely on continuous access to data for model updates because of the time and cost it takes to retrieve data from cold storage. Choosing an object storage platform that maintains immediate accessibility without sacrificing cost or performance is key to sustaining and scaling AI operations. 

Creating an AI data pipeline for success 

To build a successful and scalable AI strategy, organizations must carefully evaluate the tools and infrastructure that support each stage of the AI data pipeline. AI workflows continuously generate and reuse data, requiring solutions that are not only high-performing but also cost-effective and sustainable over time. Laying a strong storage foundation is key to ensuring that AI efforts deliver long-term value and don’t become a one-off experiment.