Datawizz Launches Continuous Learning to Turn Production Data into Model Improvements

New capability captures production signals, converts them into training-ready data, and gates releases on real-world distributions so model improvements compound over time.

SAN FRANCISCO, Jan. 29, 2026 /PRNewswire/ -- Datawizz, the platform for building, deploying, and optimizing specialized language models, today announced Continuous Learning, a new capability designed to connect production runtime data with training pipelines. Continuous Learning helps teams turn real-world signals (prompts, outputs, tool calls, traces, user feedback, and downstream outcomes) into structured training signals, enabling faster iteration without rebuilding datasets and evaluation workflows from scratch each cycle.

Most teams building specialized models follow a familiar loop: collect data, fine-tune, evaluate, deploy, and move on. Once in production, teams layer on observability, logging, guardrails, and routing, but the next iteration often restarts the pipeline. New base models arrive, traffic distributions shift, and production data grows, yet valuable signals remain stranded across dashboards, logs, and ticketing systems. As a result, retraining becomes episodic and calendar-driven rather than continuous and evidence-driven.

"Training and serving have historically lived in separate worlds," said Iddo Gino, Founder and CEO of Datawizz. "Continuous Learning bridges that gap. It captures production signals, normalizes them into training-ready data, and gates updates against what's actually hitting your endpoints today. The goal isn't to retrain more often; it's to make retraining low-friction and driven by real evidence."

How Continuous Learning works

Continuous Learning captures production signals—prompts, outputs, user feedback, tool calls, and downstream outcomes—and normalizes them into training-ready data. It surfaces high-value candidates like repeated failures, user overrides, and distribution shifts, then converts them into fine-tuning labels or preference pairs. Updates are gated against real-world traffic distributions before rollout.

Example: support workflows that improve from usage

For a customer support agent model, Continuous Learning can convert real outcomes into structured training data. Edits to suggested responses become preference signals. Reopened tickets serve as negative outcomes. Policy changes that shift traffic, like a spike in "billing cancellation" requests, can be monitored as high-priority slices. Teams can then train targeted updates and gate releases against the affected slices alongside baseline evaluation suites.

Built to handle the hard parts

Continuous learning systems have known failure modes: noisy signals, compliance constraints, overfitting to recent traffic, drift, and regressions. Continuous Learning includes quality gates, redaction policies, segmented evaluation, drift monitoring, and staged rollouts to address them. "Continuous" is configurable, not always-on, so teams can keep spend predictable.

Compounding improvement across model changes

Continuous Learning also helps teams preserve data assets across cycles. When new base models arrive or use cases evolve, teams can reuse a stream of versioned production-derived signals (preferences, outcomes, and monitored slices) so improvements carry forward rather than resetting each quarter.

To learn more about Continuous Learning, request a demo or explore resources at https://datawizz.ai.

About Datawizz
Datawizz was founded in 2025 by Iddo Gino, founder of RapidAPI. Based in San Francisco, the company builds infrastructure for specialized language models in production. The Datawizz platform helps teams train, evaluate, deploy, observe, and continuously improve domain-tuned models by turning runtime signals into structured training data and evidence-driven releases.

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SOURCE Datawizz