New Math Data streamlines machine learning workflows with automated deployment, monitoring, and optimization so your models make an impact from day one.
Training great AI/ML models is only half the battle. Getting them to run reliably in production is where the business value lives. MLOps applies the discipline of DevOps to machine learning, wrapping each model in automated pipelines for testing, deployment, monitoring, and retraining. The result is a repeatable assembly line where new ideas move from notebook to production in hours, not months, while accuracy, compliance, and costs stay under control.
In 2025,
42%
of companies scrapped most of their AI initiatives—up from 17% a year earlier—citing an inability to operationalize models.*
Just
32%
of ML models make it from pilot to production.**
MLOps turns concept models into business value across a range of industries.
Keep fraud, credit, and pricing models production-ready around the clock. Automated CI/CD, lineage-rich governance, and real-time drift alerts push models live in hours, retrain them before risk spikes, and generate audit evidence in minutes—protecting revenue and reputation without extra headcount.
Maintain load-forecasting and asset-health models as seasons, demand, and renewables shift. Edge-to-cloud pipelines, canary rollouts, and telemetry-driven alerts ensure accuracy, cut unplanned outages, and give operators real-time confidence in every dispatch decision.
Operate diagnostic and patient-risk models under strict compliance. Continuous bias tests, PHI-aware feature stores, and versioned approvals give clinicians reliable AI support and auditors click-through traceability—reducing readmissions while satisfying HIPAA and FDA scrutiny.
Support adaptive-learning algorithms with reproducible experimentation, automated deployment, and live performance dashboards. Data scientists iterate safely, educators trust AI-driven recommendations, and students benefit from steadily improving content without classroom disruption.
Our MLOps services cover every step so your models launch fast, stay reliable, and scale without surprises.
We turn ad-hoc experiments into governed products. From the first notebook to final deprecation, every model travels a managed path that captures lineage, metadata, and approvals—eliminating the “mystery model” problem and slashing time-to-value.
Automated tests, validations, and promotions move data, code, and models through environments with the push of a commit. Broken builds never hit production, and rollbacks are one click—not an all-night fire drill.
Real-time dashboards track prediction accuracy, feature drift, and data-quality signals. When thresholds are breached, alerts fire and retraining jobs spin up automatically, keeping performance (and auditors) happy.
A centralized feature store ensures that training and inference share identical, versioned transformations. Teams reuse features instead of rebuilding them, cutting duplicate work and governance headaches.
Every model version is logged with artifacts, metadata, and approval status. You always know what’s running, who approved it, and how to roll back—or A/B test—the next candidate.
Unit tests, data-quality checks, and performance benchmarks run automatically in your CI/CD pipelines. Bugs and bias are caught early, protecting customer trust.
Retraining triggers—calendar-based, performance-based, or event-driven—keep models fresh without manual babysitting. Your data scientists focus on innovation, not maintenance.
Fine-grained IAM roles, encrypted artifacts, and audit logging lock down your ML supply chain. Compliance teams sleep easier, and regulators find everything exactly where it should be.
Copyright © 2025 New Math Data. All Rights Reserved. All Rights Reserved. Please read our Privacy Policy. Content, including images, displayed on this website is protected by copyright laws. Downloading, republication, retransmission or reproduction of content on this website is strictly prohibited.
