ML Ops
Ship production models today, not next quarter.
iTuring ML Ops turns any model into a secure, governed endpoint in seconds-with approvals, lineage, rollback, and audit packs built in.
Trusted by leading banks and insurers. Built for regulated industries with audit-ready governance.
Numbers that move roadmaps.
Seconds to live endpoint
Python – R – TF – Spark – SAS – one governed process
Real-time APIs & scheduled batch scoring
Approvals – Lineage – Rollback – Audit reports
*5 Spots Left This Week
One governed flow - load, deploy, scale, govern
Load
Upload internal or external models; auto-detect framework and dependencies.
Deploy
Containerize and publish REST or batch with sample payloads and docs.
Scale
Autoscale for real-time; schedule large nightly batch runs.
Govern
Versioning, maker-checker approvals, live vs shadow, rollback, audit history.
Production without panic.
Maker-checker approvals and safe promotion/demotion.
Full evidence trail with lineage and downloadable reports.
Thresholds and alerts for model health.
Real-time, batch, and safe iteration.
Real-time REST endpoints with autoscaling.
Scheduled batch scoring for very large datasets.
Champion-challenger traffic split and one-click rollback.
Built for regulated industries - what that means.
Feature
Governance
(approvals, lineage, audit)
Frameworks supported
(Py/R/TF/Spark/SAS)
Deploy paths
(API + Batch)
Rollback / Shadow releases
Model Ops
Native, standardized
Universal, single workflow
Both first-class
One-click, no downtime
Generic Serving
Partial
Narrow set
API-only
Limited
Manual Ops
Spreadsheet-
driven
Varies by team
Ad-hoc scripts
Risky
Built to pass bank and insurer scrutiny.
Frequently Asked Questions
Which ML frameworks and file formats can ML Ops auto-deploy?
Native auto-detection covers Python pickle/conda envs, R RDS/CRAN, Spark MLlib JARs, TensorFlow SavedModel, and SAS model files. Upload a model artifact or point to a storage path—iTuring calculates the runtime, fingerprints dependencies, and publishes a standardized API contract with documentation.
Does ML Ops support both real-time scoring and high-volume batch jobs?
Yes. Deploy secure REST endpoints for interactive requests with auto-scaling, or configure orchestrated batch scoring with pre-scheduled processing that can handle large datasets. Both modes support the same governance and monitoring framework.
How do maker-checker approvals, promotions, and rollbacks work?
Each deployment follows configurable approval workflows before going live. Live vs Shadow promotion allows safe testing, while Champion-Challenger enables A/B comparisons with real traffic. One-click rollback restores the previous model version while maintaining complete audit trails for compliance teams.
What audit and observability artifacts are generated for regulatory reviews?
ML Ops automatically captures deployment history, model versioning, performance tracking, and change management logs. Generate downloadable compliance reports with complete model lineage, approval workflows, and operational metrics. Every action is timestamped and traceable for regulatory examination support.
Is last-mile business logic mandatory, or can we serve pure model scores?
Decision rules are completely optional. Deploy models to serve raw predictions, or integrate business rules when you need approval thresholds, pricing logic, or compliance flags—without redeploying the underlying model.
How does auto-scaling and performance monitoring work in production?
Set traffic-based scaling policies and performance thresholds. Real-time dashboards track model health, response times, and throughput. Automated alerts notify teams when models drift from baseline performance or scaling limits are reached.
Can we integrate existing development and deployment workflows?
ML Ops provides REST APIs for integration with existing DevOps pipelines. Connect deployment events to your workflow management systems while maintaining governance controls and approval gates.
What deployment security and access controls are available?
Enterprise-grade security measures are built-in with detailed controls available for regulated industries. Specific security architecture documentation available upon request to meet your compliance requirements.
How do we manage models across different environments and regions?
Deploy in cloud, on-premises, or hybrid configurations. Multi-environment support enables consistent model governance across development, staging, and production while maintaining regional compliance requirements.
What are typical implementation timelines and operational improvements?
Organizations report significantly faster deployment cycles and reduced operational overhead compared to manual processes. Implementation typically includes dedicated support, custom workflow configuration, and team training to ensure successful adoption.
