AI-Powered System Optimisation

The AI Balancer uses Bayesian Optimization, a probabilistic model-based approach, to efficiently explore and exploit the search space. It maintains a balance between exploration and exploitation.

The AI Balancer offers efficient iteration, precise targeting, and effective decision-making. It automates the process of fine-tuning parameters, enables users to establish specific optimisation targets and constraints, and provides a range of values for variables to explore the impact on achieving goals.

The AI Balancer was developed to streamline the efficiency of optimising parameters within complex, dynamic systems. It aims to minimise exploration time, identify optimal variable adjustments, and uncover correlations between different variables to empower users in refining their systems.

The AI Balancer uses Bayesian Optimization as its primary algorithm, along with Gaussian Process Regressor as the probabilistic model. It employs methods for efficiently exploring and exploiting the search space while tolerating stochastic noise in function evaluations.

We've rigorously tested the Machinations AI Balancer module on various types of diagrams, including those with multiple optima, solvable diagrams, and complex diagrams of existing systems. These tests ensured us that the algorithm correctly identifies solutions and achieves desired outcomes.

The Machinations AI Balancer can be activated for a team's account upon request. In order to perform an optimisation job, it consumes Prediction Plays from the available monthly quota according to your subscription plan. There are no other technical dependencies or requirements.

Machinations is ISO 27001 certified, guaranteeing stringent data security measures to protect valuable data assets. We ensure confidentiality, integrity, and availability of information, along with proactive risk management and compliance with regulatory requirements.