Multi Objective Optimisation​

EvoML Key Feature

Slide Background

Multi-Objective Optimisation feature

In our everyday life we are faced with decisions. One of the reasons why we struggle to take a decision is because, most of the time, it involves more than one objective. For instance, when buying a car, it isn’t just about buying the best car; but about buying a car that you can afford, is the right size for you, the right colour, doesn’t consume too much, it’s environmentally friendly etc. But each time you find a car that fulfils some of these criteria, it seems to lack on the other ones.

The most challenging part in this whole process arises when our objectives are overlapp ing, so we have to compromise something, although we are not sure what and to what degree. In the real business world, decisions are made up of multiple objectives which most of the time are conflicting with each other. This is where AI multi-objective optimisation comes in, which aims at finding the best solution to a given problem considering conflicting objectives. So, let’s have a closer look at why AI Multi-objective Optimisation is important for businesses, what it is and how it works.

Why multi-objective Optimisation

  1. Business problems are multi-objective problems. Almost every business decision is to maximise gains (e.g. profit margin, customer retention) under the constraint of time, budget, space, and legal and ethical boundaries.
  2. AI is about solving multi-objective optimisation problems. Accurate AI is not enough for delivering real world business value. AI needs to be efficient, explainable, ethical and beyond.

What is Multi-objective Optimisation?

Optimising An AI Model Is Like Baking A Cake!

There are three levels of AI optimisation. Most AutoML platforms can only optimise AI at the first two levels (Model Selection & Model Tunning), which are not enough for creating optimal models with multiple objectives. EvoML is the only platform embedded with Code Optimisation, generating scalable AI with high accuracy and efficiency.

How is Multi-objective Optimisation utilised?

Evolutionary Optimisation Powered by TurinTech’s Award-winning Research.

Inspired by Darwin's theory of evolution, EvoML creates and evolves thousands of candidate models. These models evolve multiple times into novel generations, and only the very smartest models for your use case will survive.

Better with Evolutionary Optimisation

  1. Multiple Objectives for Complex Problems Models are evolving towards custom criteria (e.g. accuracy, speed) from generation one.
  2. Brand new generations are created through mutation and cross-over, which can solve unique business problems. Brand new generations are created through mutation and cross-over, which can solve unique business problems.
  3. Shorter Time and Better Solutions Descendants will only be better than their ancestors. Models evolve with intention instead of trial-and-error, enabling a quicker process.
  4. Green AI for Sustainability Consume less computing resources and reduce CO2 emission during training and running.

TurinTech automates AI optimisation to make efficient AI scalable. Our EvoML platform enables businesses to automatically build, optimise with multiple objectives and deploy models within days. These smart and efficient AI models can run faster anywhere, both in the cloud and on devices, without compromising accuracy or other business metrics. Thus, businesses can easily scale AI across multiple cloud and edge devices. Learn more about AI optimisation at: https://turintech.ai