TurinTech EvoML Platform

Drive greater business value with smart and efficient AI that you can trust. Enable tech and business users to collaborate.

Why you need optimisation to scale AI?

AI model performance drifts over time as the data and business objectives change. Continuous optimisation ensures high performing models and it maximises business impact. ​In addition, training and running AI models at scale are both economically and environmentally expensive. Enterprises need to optimise AI models and software efficiency to minimise energy consumption, execution time and CPU usage, improving customer experience and business performance in the process.​ Powered by TurinTech’s proprietary research, model and software performance optimisation become available to both tech and business users.

High Speed and Low Latency

TurinTech accelerates AI models to run at high speed to deliver desired accuracy for real-time business decision making. By optimising models at source code level, businesses can minimise computational power, storage and other infrastructure resources needed to scale AI.

Run Anywhere Without Compromise

TurinTech’s multi-objective optimisation enables business to iterate their AI models on-demand based on specific criteria. Businesses can tackle difficult trade-offs between accuracy and performance, rolling out AI models to various clouds and devices at scale.

Empower Tech and Business Users

From data scientists to software engineers and business analysts, people with different levels of technical skills can generate accurate AI models instantly. With a high degree of explainability, people can trust AI as a decision-making partner to take optimal actions.

Evolutionary Optimisation Platform

Powered by TurinTech's award-winning research in evolutionary optimisation, EvoML enables enterprises to create, deploy and optimise scalable AI by automating the whole process of data science. EvoML accelerates data science lifecycle and augments your existing teams’ capability, empowering enterprises to gain a competitive edge through effective and efficient AI transformation.

Find Out How
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Scalable End-to-End Data Science Life Cycle

Data Ingestion and Exploration

EvoML can seamlessly connect to your existing data applications (such as SQL databases, object storages, HDFS, etc) through a code-free interface. EvoML automatically assesses data quality, and generates quality reports using interactive charts.This enables users to quickly analyse and better understand their data. Behind the scenes, EvoML detects outliers and imbalances in the data. EvoML is cloud-agnostic (supports Azure, AWS, Google Cloud), allowing users to easily scale cloud resources according to data volume.

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Feature Engineering

Feature Engineering is complex and critical for generating highly accurate and efficient models. EvoML automatically transforms, selects and generates the most suitable features for a given dataset. Inspired by Darwin’s theory of biological evolution, EvoML frames feature selection as natural selection, and applies Genetic Algorithms to evolve optimal features through multiple generations. Additionally, users not only have access to features built by our research team, but also those shared by other EvoML users.

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Model Selection / Optimisation

EvoML incorporates state-of-the-art machine learning models in its open-source library. It selects and tunes the optimal model for a specific business problem. Powered by our proprietary research in Evolutionary Optimisation, EvoML searches the best solution (survival of the fittest) from hundreds of thousands of models. EvoML enables multi-objective optimisation to tackle difficult trade-offs, such as accuracy of the model, execution time, complexity of the model, explainability, and other user-defined objectives.

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Code Optimisation

Speed is one of the most critical performance factors when running models in production. EvoML is the only platform that embeds code optimisation into the Data Science pipeline. In particular, EvoML automatically scans the model code to identify inefficiencies, and replaces it with optimised code to achieve the optimal speed in a given architecture. By generating energy-efficient models that can run fast, without sacrificing accuracy, EvoML enables businesses to scale AI across thousands of edge devices.

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Transparent Explanation

Explainability is critical for adopting and scaling Machine Learning. Certain models such as deep neural networks can achieve high accuracy, but cannot easily be explained (“black box”). However, trusting your model and understanding how it makes decisions is essential, before embedding it into your business processes. EvoML enables users to find a better balance between model performance and explainability. Through our interpretable interface,EvoML provides analysis and explanations of the model predictions as well as the model optimisation process.

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Deployment and Monitoring

EvoML creates production-ready models that can be automatically deployed, either as a self-running REST service, or as a local library (model code is provided). This allows different business units to successfully adopt models at scale, so as to generate predictions on real-time and continuous data. Many things can (and will) go wrong in the production environment unless they are properly monitored and controlled. EvoML monitors the model performance, checks API service health, detects model drift, and triggers retraining when models are outdated. Furthermore, EvoML allows users to improve model accuracy by correcting its decisions with new data.

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Why EvoML

An end-to-end collaborative platform for scaling AI.

Robust

Create expert-level models instantly to make real-time predictions. Empowered with advanced open source libraries.

Simple

Automated and code-free data science pipeline with rapid deployment and seamless integration within your existing systems.

Flexible

Modular design for customised process and business metrics integration for tailored objectives. Data science pipeline and optimised code generation for further customisation​​​.

Trustworthy

Transparent and explainable AI allows users to make trustworthy decisions. Models are continuously optimised and monitored for better performance.