Why Foundation Models Haven’t Replaced Classical Machine Learning

2026-05-28 · Source: The Data Exchange · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

Disarray, co-founded by Doris Xin and Moustafa Abdelbaky, addresses why classical machine learning models remain essential despite the rise of foundation models and LLMs. Their system uses agentic systems and a proprietary context graph to navigate fragmented enterprise data, spanning legacy systems, code repositories, Slack, and wikis. Disarray automates the full ML development lifecycle, from data engineering to model deployment, by stitching messy data from spreadsheets, S3, warehouses, and lakehouses into production-quality models. The co-founders highlight the limitations of AutoML and time series foundation models, emphasizing the critical role of human oversight, entity resolution, and long-horizon autonomy. Their approach aims to generate data engineering artifacts and code, ensuring model flexibility and token efficiency through its context graph memory, while maintaining human-in-the-loop supervision for safety and governance.

Key takeaway

For ML engineers and data scientists struggling with fragmented enterprise data, consider adopting context-aware agentic systems like Disarray. These systems automate the full ML lifecycle, from data stitching to model deployment, by building a comprehensive context graph from diverse sources. This approach significantly boosts productivity and token efficiency by providing high-quality hypotheses, allowing your team to tackle more projects and ensure production-quality models while maintaining human oversight for critical decisions and governance.

Key insights

Classical ML remains vital for proprietary, structured data, requiring context-aware agentic systems to navigate fragmented enterprise ecosystems.

Principles

• Data fragmentation is a core enterprise challenge.
• Context graphs significantly enhance agent performance.
• Human oversight is crucial for agentic systems.

Method

Disarray constructs a context graph from diverse enterprise data sources (code, Slack, wikis, pipelines) using static analysis and entity resolution, then employs human-in-the-loop agents to automate the full ML lifecycle.

In practice

• Integrate diverse data sources via context graphs.
• Use agentic systems for ML lifecycle automation.
• Generate notebooks for model development transparency.

Topics

• Classical Machine Learning
• Foundation Models
• Enterprise AI
• Context Graphs
• Agentic Systems
• Data Engineering

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Machine Learning Engineer, Data Scientist, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Data Exchange.