AI has become a central focus for modern enterprises, but most conversations start and stop with model training. Few are talking about what happens before and after the training phase to develop a successful AI model. How does AI get its data? What kind of data can be used to train an AI model? How does an AI model deliver better and better results over time?
AI is only as strong as the data it’s built on. Without clean, complete, and well-prepared data, even the most advanced models can underperform — or worse, produce misleading or biased results. To train AI that delivers real business value, organizations need a reliable process that ensures data is ingested, processed, and made usable with consistency and scale.
This data process — known as an AI data pipeline — has two primary phases: model training (learning from input data) and model inferencing (acting upon its training by delivering responses to prompts). In this article we’ll break down the important steps within each phase, and share what to look for in a modern AI infrastructure in order to perform both phases successfully.
The Training Phase: How AI Gets Data to Learn
AI learns by studying massive amounts of historical data to recognize patterns. But where does that data come from? The beginning of the AI data pipeline looks like this:
Data Ingestion: Data (mostly unstructured) is gathered from public online datasets (like Wikipedia and Common Crawl), customer data, industry-specific archives, file shares, object stores, and more.
Data Cleaning: This ingested data can encompass multiple formats — text, numbers, images, and videos. To make it useful for AI training, the data is filtered, cleaned, and compressed.
Data Transformation: The data is further processed to remove blacklist words, XML tags, cookies, drop-down menus, etc. The data is then structured and stored so that it’s easily accessible and AI-ready.
Training: Once the data ingestion and preparation steps have occurred, only then can an AI engine be ready to start training and developing a model catalog.
A strong AI pipeline is important because better training data produces smarter AI outcomes. An AI model is only as good as the data you feed into it.
The Inference Phase: How AI Gets Data to Make Real-World Decisions
Once a model has been trained, how does AI get the data needed to put that model into action? The inference phase is where AI interacts with live or recent data to make decisions in real-time, and then leverages that decision data to continuously improve the model.
The components of the inference phase flow directly from the learning phase steps outlined above, as follows:
Inferencing: A model is selected from the model catalog for query submission in order to get responses and insights. These queries can come directly from human users, or be submitted in real-time from connected sensors or business systems in consistent formats.
Inference Logging: The full history of model chosen, training data used, prompts made, and responses given is stored for process transparency and regulatory compliance.
Feedback Loop: The model’s delivered insights and decision data can be reviewed, labeled, and reintroduced into future training cycles to improve model performance.
Having an end-to-end AI data pipeline that covers the inference phase as well as the training phase is critical. No matter how smart an AI model is, it’s only as good as the data it sees in the moment — and it can only improve in the future if it knows how it’s performing today.
AI Pipeline Challenges
By focusing so heavily on AI training, many organizations neglect the other essential steps in the AI data pipeline process and struggle to achieve meaningful AI breakthroughs as a result. Their traditional data infrastructures store data in silos that make AI innovation difficult:
Legacy storage systems such as data lakes and data warehouses manage the data ingestion and preparation steps.
AI training is then performed in separate systems. New data inputs must continually be copied over and fed into the AI engine.
Inference takes place in yet another environment. Inference logs are stored in these separate systems and must be copied back over to the AI training platform for continuous model improvement.
Legacy data systems create the challenges of messy data, biased data, and siloed data. This in turn necessitates a heavy reliance on data copying and transfer, which introduces more time, cost, and complexity to the end-to-end process.
Modern AI success means being able to access the right data at the right time, regardless of stage. Organizations looking to make an impact with AI need one data foundation for all AI workloads.
What to Look For in an AI Data Pipeline
To understand how an AI pipeline functions end-to-end, it’s helpful to look at how each stage builds on the last. First, data is ingested from a variety of sources and centralized for access. Then, that data is cleaned and prepared to ensure it’s accurate, relevant, and usable, and fed into the training process. Lastly, the inferencing step takes place, with its results logged and used for continuous model improvement.
The best results are achieved when all of this happens in one seamless process. When looking to establish or improve their AI infrastructure, organizations should prioritize platforms that offer the following characteristics:
Unification: A single platform that combines disparate systems and removes complexity from the process.
Flexibility: The ability to ingest data from structured and unstructured sources, and process it together so that it’s AI-ready.
Speed: Perform more simultaneous inferencing, allowing you to do more work in the same amount of time.
Security: A unified security context across all data types and lifecycle stages.
Why a Unified Data Strategy Matters
Companies that understand and invest in the full scope of the AI pipeline are unlocking significantly more value from their AI investments. VAST Data helps organizations like Pixar and CoreWeave build data foundations for AI that scale from idea to impact. Schedule a personalized demo with our team today to see how a modern, unified AI data pipeline can bring your AI goals to life.
