Announcing the Ultimate Guide to AI Data Pipelines

When we started MLtwist three years ago, the AI data revolution had not yet gone mainstream.

Data for artificial intelligence was a well served but niche sector of the larger $200 billion global data industry whose moment arrived in late 2022 with the release of ChatGPT. This release democratized access to an ever-increasing number of large language models, and a rapidly expanding marketplace of AI-based apps, products, and services.

Here are some numbers that paint a picture of an economic and technological transformation already well underway:

– Over 80% of the data driving the development of AI products and services is unstructured. Business documents, website pages, emails, social media feeds, 3D images, audio, and video are just some of the data types used to develop, train, and refine AI models. 

– The global market for the data collection and labeling essential to training AI models will grow year on year at 24.9% until 2030, when it will be worth over $15 billion.

– The International Data Corporation predicts that the volume of this unstructured data will grow to 175 zettabytes by 2025 – that’s enough to fill more than 20 1-terabyte hard drives for every person on Earth.

It’s undeniable: We are swimming in an ocean of data. And it is just as undeniable that legacy ETL pipelines aren’t the right fit for processing that data for AI.

Despite data’s wealth of untapped potential, in 2024 most data preparation for AI applications is still manual, labor-intensive, and inefficient. The volume of unstructured data and the market for it are growing exponentially, but most workflows for leveraging its value are not up to speed.

– Data scientists report spending up to 80% of their time on preparing data as opposed to developing, training, and refining models. 

– Seventy-six percent of those scientists report that data preparation is the least enjoyable part of their work. 

– Studies from Wakefield Research show that organizations incur financial losses each year due to inefficient data processing and pipeline maintenance.

One thing was clear from our years of AI data and machine learning experience: Manual workflows for extracting structured information from raw data are no match for the dataflow from the billions of devices powering the planet. For one, they cannot provide the synchronous updating required at each step of the process needed to prepare data for AI model training. The infrastructure needed to process AI data at the scale required in 2024 is different from anything that has come before.

Data pipelines for AI require specific features, architecture, and processes that are not part of traditional ETL pipelines. Gartner has reported that only 4% of the world’s data is AI-ready. That’s largely an infrastructure problem that involves not only architecture, but also has to integrate the human-in-the-loop process that guarantees high quality training data.

This is the genesis of the Ultimate Guide to AI Data Pipelines, our soon-to-be released e-book. We present the facts and figures around AI data in 2024 and dissect the workings of an AI data pipeline to show you, step by step, what’s involved in transforming raw bytes of multi-modal information into AI-ready datasets. 

With the world’s leading experts weighing in, this guide is the product of years of accumulated expertise, knowledge, and conversations with clients, colleagues, and researchers. We hope it broadens your perspective on the size and the scope of the infrastructure needed for the biggest technological and economic transformation ever witnessed and what it all means for your organization.