The most powerful AI models are only as effective as the AI pipeline architecture behind them. From data ingestion to deployment, every step of the process shapes how fast, accurate, and scalable those models can be in production.
AI development involves transforming raw data into meaningful intelligence. The quality of the intelligence is dependent on the quality of the data being fed into the training process. This is what makes a thoughtful AI data pipeline so critical for AI scalability and success.
In this article we’ll examine what exactly an AI pipeline architecture is, the components it consists of, the challenges organizations commonly face in getting one set up, and how to build a scalable pipeline that’s ready for AI innovation.
What is an AI Pipeline?
An AI pipeline is the series of processes and transformations that data undergoes from its raw state to a refined form that’s ready to train AI models and support inferencing and decision-making. These processes include all of the necessary pre-training data readiness steps — data ingestion, cleaning, reduction, and storage — as well as the essential post-training activities that bring an AI model to life — inferencing, data logging, and fine-tuning.
The more automated, nimble, and scalable an AI pipeline architecture is, the faster an organization can implement and refine their AI workflows. A robust, end-to-end pipeline can be a true differentiator in terms of increasing market competitiveness as well as AI ROI.
Understanding AI Pipeline Architecture
The core components of an AI data pipeline architecture are as follows:
Ingestion Streams: Data is collected into the system from multiple internal and external data sources — online datasets, customer data, object stores, and more — encompassing both structured and unstructured data.
Database Environment: The ingested data can encompass multiple formats, including text, numbers, images, and videos. Therefore, the incoming data is filtered, cleaned, processed, and compressed to make it consistent and ready for use in model training.
GPU Cluster: Data is fed into multiple GPUs to accelerate the computational processes involved in AI training operations. Distributing the training workload across the cluster enables exabyte scale, millisecond latency, and checkpoint windows.
Distribution Catalog: Once trained, AI models are compiled in a centralized global repository, available from any geographic location for inferencing. This catalog provides users with the ability to evaluate, compare, and deploy models suitable for their specific tasks.
Content Archive & Model Logs: The full history of model chosen, training data used, prompts made, and responses given is stored for process transparency and regulatory compliance. The model’s decision data can also be reviewed, labeled, and transferred back to GPU Clusters for continuous model fine-tuning and re-training.
An AI pipeline architecture differs from a traditional ETL (Extract, Transform, Load) pipeline in a number of ways:
Minimized Data Movement: With an end-to-end AI pipeline, all data processes occur within a single platform. Data never needs to be moved to other systems.
In-Place Transformation: All data cleaning, formatting, and reduction processes occur within the same database housing the collected data. No data copying required.
No Need to Load Data: Because data hasn’t been moved, in an AI pipeline the data transfer and loading steps are eliminated, saving time and cutting the cost of redundant systems.
Common Challenges in AI Pipelines & How to Overcome Them
Organizations looking to invest in an end-to-end AI data pipeline often wrestle with the following obstacles:
Fragmented, Siloed Data
Huge quantities of data are needed for effective AI training, but this data is usually disorganized and spread out. It exists in multiple structured and unstructured forms — text, numbers, images, and videos — and is often siloed within completely separate sources and systems — public online datasets (like Wikipedia), customer data repositories, industry-specific archives, file shares, object stores, and more. AI pipelines tackle this problem by filtering, formatting, cleaning, and organizing all data as soon as it’s ingested, creating a uniform data stream that’s ready for AI training.
System Migration
Having amassed deep expertise in their existing data systems, organizations can be reluctant to replace them despite the performance bottlenecks they cause. In these circumstances, a phased approach can be taken over time — such as starting first with AI storage — until the entire data flow is running through an AI-ready pipeline that eliminates the need to move data between siloed systems.
Ensuring Data Integrity & Compliance
Organizations can feel uneasy about trusting a new system with their data. However, cutting-edge AI data pipelines are built with cutting-edge security features — multi-tenancy, encryption, immutable snapshots, and more. VAST, for example, also prioritizes data governance and compliance, giving AI teams full control and transparency throughout the entire data processing lifecycle.
Implementation Costs
Implementing any new system requires an up-front investment. However, by consolidating multiple data systems into one, and greatly accelerating AI training and inference times, high-performance AI data pipelines such as the VAST Data Platform lower the total cost of ownership, increase ROI, and improve market competitiveness for organizations.
Next Steps: How to Build a Scalable AI Pipeline
To get started on your AI journey, it’s important to first assemble an end-to-end AI data pipeline. In doing so, some key pipeline infrastructure considerations to take into account include:
AI Storage: As AI models process unprecedented volumes of data to learn, test, and evolve, a new approach to storage is necessary to capture, process, and manage all that information. To accomplish this task, AI data storage technology typically leverages flash-based storage, advanced data reduction techniques, and linear scaling multi-protocol performance to achieve optimal data processing speeds at exabyte-scale.
Compute Power: Processing all of this data requires a great deal of computational power, in the form of large-scale GPU clusters. But perhaps more important than raw GPU performance is cluster management and efficiency. Leading AI pipeline platforms leverage a linearly scalable parallel architecture to easily and consistently deliver more throughput than GPUs require, while continually maximizing usable storage capacity and optimizing GPU usage to lower storage costs for organizations.
Process Automation: An AI data pipeline should be able to operate continuously with minimal human input. For example, to support the model training phase, the ingestion of new data should trigger the configured data preprocessing and transformation operations. Additionally, AI pipelines should support the iterative nature of AI development, automatically feeding inference query analytics back into the model for continuous improvement and refinement based on real-world feedback.
Many modern AI-driven organizations like Core42 and the NHL are solving AI pipeline challenges with VAST Data. VAST offers one multi-protocol data processing system with unified permissions and data management. This provides organizations with the benefits of having just one system to buy, one system to manage, and no need to move data between separate prep, training, and inference spaces — lowering overall costs and increasing the time-to-value of AI projects.
Schedule a personalized demo with our team today to see how a modern, unified AI pipeline architecture can bring your AI goals to life.
