DecisionOps
Accelerate time to value for developer-authored decision models with the testing, observability, and governance tools your team needs to build trust and deliver value.
Operationalize decision models faster, while keeping them tested, observable, and governed.
DecisionOps on FICO® Platform gives data science and IT teams one governed workspace for testing, observing, managing, and scaling developer-authored optimization, simulation, heuristics, and rules-based decision models. Teams can move from first commit to production in days instead of months, backed by a native, developer-first foundation on FICO Platform, so every model earns stakeholder trust and delivers business value as it scales.
One lifecycle for every decision model
Deliver consistent model testing, observability, collaboration, and management at enterprise scale.
Unify optimization, simulation, heuristics, and rules-based decision apps in one collaborative workspace, using the solvers and tools your team already relies on.
Explore what-if scenarios, run online shadow tests, and integrate with CI/CD workflows to ensure quality and reduce rollout risk.
Maintain a complete audit trail of every model run to help meet compliance standards and quickly reproduce issues to shorten MTTR.
Manage configurations across environments, regions, and use cases through a common API layer, cutting required engineering effort by at least 50%.
Your decision problems may vary, but your path to production stays the same.
Whether a model optimizes delivery routes, sets dynamic prices, or schedules a workforce, it has to survive the same journey: move out of a developer’s local environment, get tested before it touches a live business process, stay observable once it’s running, and adapt as conditions change. DecisionOps gives every decision model a repeatable path to deployment, so teams spend less time rebuilding infrastructure and more time improving the models themselves.
DecisionOps drives better outcomes that scale
DecisionOps brings fully featured, integrated tooling for decision model experimentation, governance, and observability together into FICO Platform.
From experimentation to governance to observability, FICO brings every stage of the decision model lifecycle together, so your team spends more time improving models and less time managing tooling.
Gartner, Forrester, and IDC independently recognize FICO as a leader in the decision intelligence platform market, giving your team a vendor choice evaluated by outside experts.
Explore more
Optimization
Unleash the power of mathematical optimization — find solutions to your most complex business problems using high-performance solvers that maximize your desired outcomes.
Simulation
Unlock a distinct competitive advantage with an enterprise scenario management ecosystem.
AI & Analytics
Deploy fully interpretable, real-time analytics and ML and leverage the very best AI models in the market today.
Frequently asked questions
How does DecisionOps differ from MLOps, and can MLOps tools manage optimization and decision models?
DecisionOps manages the full lifecycle of decision models, including optimization models, rules engines, simulations and heuristics, while MLOps manages the machine learning training lifecycle. MLOps tooling can support the machine learning steps within a decision workflow, and decision models can connect to existing MLOps pipelines, but MLOps tools are not built to manage the full decision model lifecycle. Decision models also require decision quality testing against business KPIs, solver management, and decision level audit trails.
Organizations deploy optimization models faster by applying CI/CD to decision models, which are developed like any other modern software. A CI/CD workflow for optimization models involves five components: Git based code management, infrastructure that serves each model as a service through API endpoints, testing tools, model management with versioning and rollback, and shared collaboration features. Every code change then triggers automated tests, which helps ship higher quality models to production faster. DecisionOps provides a holistic approach to deploying and scaling decision models with CI/CD incorporated.
Validation combines offline and online testing. Offline tests include scenario tests, which simulate what-if questions, and acceptance tests, which check results against defined thresholds as part of CI/CD. Online tests use production data to reduce risk before a live rollout. This includes shadow testing, which runs a candidate model on production data without affecting production systems, and switchback testing, which is an A/B-style test of model performance in production.
Yes. Enterprise DecisionOps integrates with multiple solvers and other decision technologies, so organizations can deploy, test, and monitor decision applications built on different solvers and modeling frameworks in one workspace.
Common use cases include logistics and supply chain optimization, vehicle routing and last-mile delivery, staffing and scheduling, and price optimization. Organizations can start with a pre-built application for routing, scheduling, packing or price optimization, or bring their own developer-authored model code, then test and deploy it through the same DecisionOps workflow.
DataOps is a collaborative data management practice that improves the communication, integration, and automation of data flows between data managers and data consumers. DecisionOps is a set of practices, tools, and infrastructure for operationalizing decision models quickly, consistently, and reliably. DataOps focuses on the quality and delivery of data, while DecisionOps focuses on the lifecycle of the models that act on said data, including testing, deployment, observability and governance of optimization models, rules engines and simulations.