While the headlines of the ongoing AI revolution are often dominated by large language models and generative software, the silent war is being fought in the data center. The hardware required to feed, train, and infer upon these models is […]
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If you lead an enterprise data science team or a quantitative research unit today, you likely feel like you are living in two parallel universes.
In one universe, you have the “GenAI” explosion. Chatbots now write code and create art, and boardrooms are obsessed with how large language models (LLMs) will change the world. In the other universe, you have your day job: the “serious” work of predicting churn, forecasting demand, and detecting fraud using structured, tabular data.
For years, these two universes have felt completely separate. You might even feel that the GenAI hype rocketship has left your core business data standing on the platform.
But that separation is an illusion, and it is disappearing fast.
Whether you are a skeptic or a true believer, you have most certainly interacted with a transformer model to draft an email or a diffusion model to generate an image. But while the world was focused on text and pixels, the same underlying architectures have been quietly learning a different language: the language of numbers, time, and tabular patterns.
Take for instance SAP-RPT-1 and LaTable. The first uses a transformer architecture, and the second is a diffusion model; both are used for tabular data prediction.
We are witnessing the emergence of data science foundation models.
These are not just incremental improvements to the predictive models you know. They represent a paradigm shift. Just as LLMs can “zero-shot” a translation task they weren’t explicitly trained for, these new models can look at a sequence of data, for example, sales figures or server logs, and generate forecasts without the traditional, labor-intensive training pipeline.
The pace of innovation here is staggering. By our count, since the beginning of 2025 alone, we have seen at least 14 major releases of foundation models specifically designed for tabular and time-series data. This includes impressive work from the teams behind Chronos-2, TiRex, Moirai-2, TabPFN-2.5, and TempoPFN (using SDEs for data generation), to name just a few frontier models.
Traditionally, machine learning models were treated as static artifacts: trained once on historical data and then deployed to produce predictions.
That framing no longer holds. Increasingly, modern models behave less like predictors and more like model-generating systems, capable of producing new, situation-specific representations on demand.
We are moving toward a future where you won’t just ask a model for a single point prediction; you will ask a foundation model to generate a bespoke statistical representation—effectively a mini-model—tailored to the specific situation at hand.
The revolution isn’t coming; it’s already brewing in the research labs. The question now is: why isn’t it in your production pipeline yet?
If you’ve scrolled through the endless examples of grotesque LLM hallucinations online, including lawyers citing fake cases and chatbots inventing historical events, the thought of that chaotic energy infiltrating your pristine corporate forecasts is enough to keep you awake at night.
Your concerns are entirely justified.
While the new wave of data science foundation models (our collective term for tabular and time-series foundation models) is promising, it is still very much in the early days.
Yes, model providers can currently claim top positions on academic benchmarks: all top-performing models on the time-series forecasting leaderboard GIFT-Eval and the tabular data leaderboard TabArena are now foundation models or agentic wrappers of foundation models. But in practice? The reality is that some of these “top-notch” models currently struggle to identify even the most basic trend lines in raw data.
They can handle complexity, but sometimes trip over the basics that a simple regression would nail it–check out the honest ablation studies in the TabPFN v2 paper, for instance.
While these models still face early limitations, there are compelling reasons to believe in their long-term potential. We have already discussed their ability to react instantly to user input, a core requirement for any system operating in the age of agentic AI. More fundamentally, they can draw on a practically limitless reservoir of prior information.
Think about it: who has a better chance at solving a complex prediction problem?
Classical machine learning models (like XGBoost or ARIMA) do not suffer from the “hallucinations” of early-stage GenAI, but they also do not come with a “helping prior.” They cannot transfer wisdom from one domain to another.
The bet we are making, and the bet the industry is moving toward, is that eventually, the model with the “world’s experience” (the prior) will outperform the model that is learning in isolation.
Data science foundation models have a shot at becoming the next massive shift in AI. But for that to happen, we need to move the goalposts. Right now, what researchers are building and what businesses actually need remains disconnected.
Leading tech companies and academic labs are currently locked in an arms race for numerical precision, laser-focused on topping prediction leaderboards just in time for the next major AI conference. Meanwhile, they are paying relatively little attention to solving complex, real-world problems, which, ironically, pose the toughest scientific challenges.
Here is the crux of the problem: none of the current top-tier foundation models are designed to predict the joint probability distributions of several dependent targets.
That sounds technical, but the business implication is massive. In the real world, variables rarely move in isolation.
The world is a messy, tangled web of dependencies. Current foundation models tend to treat it like a series of isolated textbook problems. Until these models can grasp that complexity, outputting a model that captures how variables dance together, they won’t replace existing solutions.
So, for the moment, your manual workflows are safe. But mistaking this temporary gap for a permanent safety net could be a grave mistake.
The missing pieces, such as modeling complex joint distributions, are not impossible laws of physics; they are simply the next engineering hurdles on the roadmap.
If the speed of 2025 has taught us anything, it is that “impossible” engineering hurdles have a habit of vanishing overnight. The moment these specific issues are addressed, the capability curve won’t just inch upward. It will spike.
Despite the current gaps, the trajectory is clear and the clock is ticking. The wall between “predictive” and “generative” AI is actively crumbling.
We are rapidly moving toward a future where we don’t just train models on historical data; we consult foundation models that possess the “priors” of a thousand industries. We are heading toward a unified data science landscape where the output isn’t just a number, but a bespoke, sophisticated model generated on the fly.
The revolution is not waiting for perfection. It is iterating toward it at breakneck speed. The leaders who recognize this shift and begin treating GenAI as a serious tool for structured data before a perfect model reaches the market will be the ones who define the next decade of data science. The rest will be playing catch-up in a game that has already changed.
We are actively researching these frontiers at DataRobot to bridge the gap between generative capabilities and predictive precision. This is just the start of the conversation. Stay tuned—we look forward to sharing our insights and progress with you soon.
In the meantime, you can learn more about DataRobot and explore the platform with a free trial.
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The shift from prototyping to having agents in production is the challenge for AI teams as we look toward 2026 and beyond. Building a cool prototype is easy: hook up an LLM, give it some tools, see if it looks like it’s working. The production system, now that’s hard. Brittle integrations. Governance nightmares. Infrastructure wasn’t built for the complexities and nuances of agents.
For AI developers, the challenge has shifted from building an agent to orchestrating, governing, and scaling it in a production environment. DataRobot’s latest release introduces a robust suite of tools designed to streamline this lifecycle, offering granular control without sacrificing speed.
New features in DataRobot 11.2 and 11.3 help you close the gap with dozens of updates spanning observability, developer experience, and infrastructure integrations.
Together, these updates focus on one goal: reducing the friction between building AI agents and running them reliably in production.
The most impactful areas of these updates include:
The sections that follow focus on these capabilities in detail, starting with standardized connectivity, which underpins every production-grade agent system.
Agents break when tools change. Custom integrations become technical debt. The Model Context Protocol (MCP) is emerging as the standard to solve this, and we’re making it production-ready.
We’ve added an MCP server template to the DataRobot community GitHub.
Everyone is building RAG. Almost nobody is building RAG with RBAC, audit trails, and the ability to swap models without rewriting code.
The “Talk to My Docs” application template brings natural language chat-style productivity across all your documents and is secured and governed for the enterprise.
Getting an agent into production should not require days of scaffolding, wiring services together, or rebuilding containers for every small change. Setup friction slows experimentation and turns simple iterations into heavyweight engineering work.
To address this, DataRobot is introducing an agentic application starter template and CLI, both designed to reduce setup overhead across both code-first and low-code workflows.
As prompts move from experiments to production assets, ad hoc editing quickly becomes a liability. Without versioning and traceability, teams struggle to reproduce results or safely iterate.
To address this, DataRobot introduces the Prompt Management Studio, bringing software-style discipline to prompt engineering.
As AI agents scale across teams and workloads, visibility and control become non-negotiable. Without clear insight into resource usage and enforceable limits, performance bottlenecks and cost overruns quickly follow.
As teams experiment with agent behavior and reasoning, access to multiple foundation models becomes essential. Managing separate credentials, rate limits, and integrations across providers quickly introduces operational overhead.
Jira and Confluence connectors: To power your vector databases, DataRobot provides a cohesive ecosystem for building enterprise-ready, knowledge-aware agents.
NVIDIA NIM Integration: Deploy Llama 4, Nemotron, GPT-OSS, and 50+ GPU-optimized models without the MLOps complexity. Pre-built containers, production-ready from day one.
Milvus Vector Database: Direct integration with the leading open-source VDB, plus the ability to select distance metrics that actually matter for your classification and clustering tasks.
Azure Repos & Git Integration: Seamless version control for Codespaces development with Azure Repos or self-hosted Git providers. No manual authentication required. Your code stays centralized where your team already works.
If you’re already a customer, you can spin up the GenAI Test Drive in seconds. No new account. No sales call. Just 14 days of full access inside your existing SaaS environment to test these features with your actual data.
Not a customer yet? Start a 14-day free trial and explore the full platform.
For more information, please visit our Version 11.2 and Version 11.3 release notes in the DataRobot docs.
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