Journey Maps Out New Route for Fundamental AI Model Development
In the world of AI, things are moving at an unfathomable pace. A few short years ago, the tech industry was still dreaming about the possibilities of advanced AI functions, which seemed like a far-off fantasy. But time waits for no one, and now we're living in the era of agentic AI tools, a reality that captured everyone's attention in this year's tech hype cycle.
And it's not just the user interface that's changing; the foundational infrastructures, or core data repositories, powering AI are also undergoing significant transformation. As Zuzanna Stamirowska, the CEO and co-founder of Pathway, puts it, we're transitioning into an era where more intelligent systems stand at the forefront, not necessarily larger models.
Stamirowska, whose company specializes in AI data pipelines, pointed out that the AI model market will evolve into an "xLM" market, with the "x" standing for anything, any form, any size, any domain specialization, and almost anything else you can imagine. This means AI will be a versatile tool capable of adapting to various scenarios and industries.
The AI Model Buffet
AI models are set to become large, small, portable, hybrid, remote, and domain-specific, while use cases will grow increasingly diverse with varying levels of price, security, and latency sensitivity.
Stamirowska highlights that this next wave of models promises advanced reasoning capabilities. While this is an exciting development, it also presents significant challenges for the underlying data infrastructure that supports AI models.
The primary struggle for software and data engineering in this space involves creating architectures that manage structured and unstructured data types, streaming data, and real-time updates. While maintaining rigorous governance and security requirements.
Real-Time Data Engineering
As AI models evolve, real-time data engineering could put significant pressure on resources, especially for organizations that rely on static batch data uploads. The arduous task of plumbing the data pipeline with integrated, transformed data would be more time-consuming, and with increased demand for to-the-moment accuracy, the challenge becomes even more daunting.
But help is on the horizon, and the industry has begun to use the term "live AI" to describe data engineering that focused on faster-moving, living data. Live AI enables constant learning and unlearning while reducing pressure on data engineering teams, allowing them to focus on more innovative tasks.
What is Live AI?
Live AI achieves this by transitioning from static to live pipelines through hybrid systems that leverage both batch processing and live data connectors. This shift not only enhances model accuracy but also reduces data engineering burdens.
Recognizing this shift, Monica Ortiz, a data engineering professional, recommends that organizations design their systems appropriately, anticipating that most future systems will be real-time-ish and built for streaming-native application functions. To implement real-time systems, data infrastructure needs to be robust, so a more modern strategy is necessary.
Data Engineers Step Up
As we move forward, Stamirowska predicts that the role of data engineers will evolve from protecting data to stewarding it, helping organizations make informed strategic decisions and drive pipeline innovation.
In conclusion, the future of AI is bright light years ahead, and with the emergence of live AI, we are equipped with the tools needed to navigate the complexities of AI data engineering. By adopting frameworks that enable automation and intelligent data management, data engineers can focus on innovation rather than repetitive manual tasks, unlocking further AI-driven efficiency.
- To effectively adopt and utilize these evolving AI models, organizations will need to rely on robust frameworks that can manage both structured and unstructured data, streaming data, and real-time updates.
- The advent of foundation models and advanced AI functions has led to the development of various AI models, such as large, small, portable, hybrid, remote, and domain-specific models, which can enable fanciful use cases in various industries.
- As AI models continue to evolve and become more versatile, the role of data engineers will shift from solely focusing on data protection to stewarding data, enabling organizations to make strategic decisions and drive pipeline innovation.
