ACTICO Machine Learning

Software platform for intelligent decision automation using machine learning and business rules

Machine Learning

Integrated Platform for Machine Learning

ACTICO Machine Learning is a software platform with integrated components for applying machine learning. The platform provides support throughout the process from data preparation to using the trained machine learning models in production systems.

Easy access to leading machine learning technology

Advanced machine learning algorithms and approaches

Combination of machine learning and business rules



Monitors showing DMN decision modeling, scoring results and the training UI in ACTICO Machine Learning




Introduction to Machine Learning with ACTICO


Step 1: Creating the business decision model

Decision Modeling

Business decision modeling is the basis for intelligent decision automation. The decision model takes into account all aspects relevant to make the business decision. Some aspects are defined in the form of graphical business rules, others are learned automatically and implemented in the form of machine learning models. The decision model shows very transparently where machine learning is used.

Step 2: Preparing data through feature engineering

Feature Engineering

To create a good machine learning model, data scientists need to prepare relevant data through feature engineering. With its rules-based approach, ACTICO Modeler simplifies this often cumbersome process step and enables automated model retraining. In the next step, the prepared data is used to train the machine learning model.

Step 3: Training the model

Model Training

Training describes the learning process. Data scientists select a machine learning algorithm, such as Gradient Boosting or Deep Learning, that is used to automatically learn from the prepared data. ACTICO offers an easy configuration and visualization of the training process. If the results are good, the machine learning model can be stored for management with a click of a button.


Graphic showing the machine learning roundtrip
Step 4: Managing and governing machine learning models

Model Management

The trained machine learning model is stored and managed in a centralized repository together with the business decision models and rule models. Users can administer and govern these models depending on their roles and permissions. A variety of governance features helps control, monitor and document activities across the model life cycle.

Step 5: Executing the machine learning model

Execution

Execution means that the machine learning model is used for automated decision-making. ACTICO supports one-click model deployments and enables their fast execution. Machine learning models can be executed in various scenarios including the provisioning of services, batch processing or the direct integration of the machine learning engine.

Step 6: Automating the machine learning process to realize adaptive learning

Adaptive Learning

By executing the machine learning model in day-to-day business operations, new data is collected constantly that can be used to improve the model. Adaptive learning automates the machine learning process from feature engineering via retraining to deploying the model. Machine learning thus becomes efficient and continuously introduces new knowledge to improve business decisions.






Key Features

Combined Approach

ACTICO Machine Learning relies on the combination of machine learning and business rules. Generated machine learning models are orchestrated together with business rule models and executed automatically in any software solution.

Leading Technology

ACTICO Machine Learning provides easy access to advanced algorithms and approaches for a wide range of applications. The platform relies on powerful technologies to enable better predictions and drive automation.

Automated Retraining

ACTICO Machine Learning enables the automatic retraining of machine learning models and facilitates their rapid deployment. This way, new data can be leveraged to continuously improve predictive models, and a self-learning system can be realized.


costumer

What is intelligent decision automation and how do financial institutions benefit from machine learning?


Benefits

Agility

ACTICO offers high agility when applying machine learning. The integrated platform streamlines the process and allows users to run through the entire process with just a few clicks. Companies can realize self-learning decisions and accelerate the time to market.

Ease of Use

ACTICO offers a high ease of use and a trouble-free application of machine learning. With graphical decision modeling, rules-based feature engineering, UI-supported training and 1-click deployments, even users with limited technical skills can use artificial intelligence.

Scalability

ACTICO technology is built for enterprise-scale scenarios that require high scalability and availability. In-memory technology and clustering allow for high performance of both the machine learning process and the automated execution of the resulting models.



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