Transform your models into robust applications
See why analysts, data scientists, and engineers choose Modelshop to rapidly develop models that scale as software applications.
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Hello applications, goodbye long development cycles!
The rules have changed. Now, subject matter experts can deploy models as software applications in just a few weeks to manage priority requirements and automate stand-alone approaches.
Deploy models into production 80% faster
Go from concept to prototype, to productionized model in a single platform.
Build models in a production-ready environment to skip long development cycles.
Domain experts can implement concepts quickly using code-free and code-friendly tools.
Unlike traditional methods, stages of the modeling process can occur in parallel.
Leverage existing assets and build upon them
Use existing data sources, logic, and predictive models. Layer on new models, variables, and rules using code-free or code-friendly development.
Empower subject matter experts
SMEs can implement concepts into business processes with or without development support. Knowledge hand-offs are things of the past!
Blend, test, and debug as you build
Unlike traditional methods, stages of the modeling process can occur in parallel. Blend data, test, and debug during model development.
Ongoing optimization of models
Adapt on-the-fly to meet changing requirements. Remove cumbersome re-coding for every change.
“Modelshop gives us full flexibility to determine how credit decisions are made. I can run simulations and test new rules with incredible speed. It helps my team deliver results in real-time to provide a better customer experience.”
Sonia Steinway, Outside Financial
“Modelshop adeptly solves a universal business need by enabling companies to scale their models into production with a keen focus on speed, flexibility and transparency. With Modelshop’s simple yet robust UI and modeling framework, we were able to deploy a mission-critical decision engine and drive meaningful change for our business.”
Mark Levine, Veros Credit
“I was able to cut the amount of time I spend manually cleaning data in half, freeing up my time for work that actually matters.”
Lee Lattimer, Loan Science
So what does this look like?
Step 1: Access Data From Any Source
Link data no matter where it’s coming from (Oracle, AWS, Hadoop, CSV, etc.) Prep it for analysis by viewing entire tables and using the wizard to link datasets together.
Step 2: Create Variables, Rules, and Predictors
Leverage existing assets and add to them using code-free or code-friendly tools. Since you're building in a production-ready environment, you can prep, test, and optimize as you build.
Step 3: Deploy Models as Applications
In a couple of clicks, deploy your model as a scalable application, no long dev cycles required. Export data and models easily. The output can be on-demand or real-time reporting, XML, or JSON, it’s your choice.
Give your team an edge
High-growth companies are scaling their repetitive analysis, adapting to rising complexity, and complying with regulatory requirements faster than ever.
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