Simplify ML deployment with Decisimo: A clear platform for decision making - Decisimo

Published on: 2024-08-10 18:36:09

Decisimo as a tool for deploying ML models

Decisimo is a practical platform for deploying machine learning models trained in Python or R.

It lets you integrate these models into a broader decision-making process, which makes it useful for businesses and organizations that rely on machine learning for decisions.

Building Decision Flows with Models

One of the main capabilities of Decisimo is building a decision flow. That makes it straightforward to place a model trained in Python or R inside a broader decision-making process, such as probability assessment or segmentation.

Decisimo also supports optimization models, which can help determine the best offer or combination and make the decision-making process easier to manage.

Chaining Models and Incorporating Decision Components

Another useful capability of Decisimo is chaining models together into decision logic.

This means multiple models can run within a single decision-making process, which can improve consistency and reliability.

Decisimo also supports other decision components, such as rule sets, decision tables, and mathematical functions, which adds more control to the decision-making process.

Examples from various industries and applications

Decisimo's ability to build decision flows and incorporate pre-trained models makes it a flexible platform for a wide range of industries and applications.

Credit Scoring Model in Consumer Lending

For example, in consumer lending, a machine learning model trained in Python or R could be used to predict the creditworthiness of an applicant based on their financial history and other factors.

That output could then be incorporated into a decision flow in Decisimo, where it would be used to determine the appropriate interest rate or loan terms for the applicant.

Risk-based Pricing and Product Proposal

The model output could also be used to propose a different product or a lower or higher down payment.

This risk-based pricing approach can help lenders make better lending decisions while also supporting fair and transparent lending practices for borrowers.

Marketing and Customer Service

Decisimo can also be used in marketing, customer service, and many other fields. For example, in customer service, a machine learning model trained in Python or R could be used to predict the likelihood of a customer requesting a refund.

That probability output could then be incorporated into a decision flow in Decisimo, where it would be used to determine the best course of action, such as providing a discount or offering an alternative product.

Advantages of Decisimo over traditional ML operations

Decisimo offers a clear advantage over traditional machine learning operations, where a separate model platform must be built and then integrated with other decision support systems.

With Decisimo, machine learning models and decision logic sit together in one platform, giving teams a single place to manage decisions.

This removes the need for complex, time-consuming integration work and supports a simpler, more efficient approach to decision-making.

User-friendly Interface and Management of the Decision-making process

Decisimo also provides a clear interface for managing the decision-making process.

This makes it easier for teams to collaborate on decision-making projects and supports more transparency and accountability.

No-code Platform and Business User Empowerment

One of the main benefits of Decisimo is that it does not require coding skills or knowledge of complex topics such as programming languages or cloud operations.

It is a no-code platform that lets business users update decision logic, change rules, and deploy quickly to production.

This means non-technical users can make changes to decision-making processes and keep them aligned with changing business needs.

Decisimo as a complete decision-making solution

Overall, Decisimo is a practical platform that simplifies deploying machine learning models and placing them inside a broader decision-making process.

It provides a complete decision-making solution that removes the need for complex integration work and supports a simpler, more efficient approach to decision-making.

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