Automatically Generate and

Optimise Your ML Code

Generate top-performing ML model code quickly and effortlessly by simply uploading your data. evoML will automatically optimize its speed and memory usage for your targeted hardware.

step 1

From Raw Data to ML data

Data Cleaning

Detect anomalies and remove unnecessary variables in your dataset.

Feature Selection

Automatically transform and select the most suitable features for a given dataset.

Feature Generation

Evolve optimal features through multiple generations, inspired by Darwin’s theory of biological evolution.

step 2

From ML Data to ML model

Parallel Model Code Training

Create thousands of candidate model code in parallel. Automatically recommend the ones that better fit your business problem.

Hyperparameter Tuning

Fast tune millions of model code parameters for the best performance.

Multi-objective Optimisation

Improve model code performance and speed without sacrificing accuracy or explainability.

step 3

Production-ready ML Code

Quality Assurance

Automated code testing to ensure stability and reliability in production.

Performance Benchmark

Evaluate your model code's throughput and latency in different scenarios (e.g. data volume, hardware).

Deploy Anywhere

Portable packaging that can be automatically deployed: as API, in the database, or as a local library.

Code Download and Customisation

Customise model code for your deployment criteria to better fit your business needs.

step 4

Optimise your data transformation

Code-level acceleration

Identify the most expensive lines of code and automatically optimise them with more efficient code alternatives.

Model code-level acceleration

Optimise the speed of your autoencoders or transformers by automatically converting them to more efficient formats (such as Pytorch, ONNX, etc) for your hardware (such as GPU)

ML-based acceleration

Continuous active learning from previous experiments to predict the most efficient data transformations. Use AI to optimise your AI.

step 5

Optimise your Model Training and Prediction Time

Code-level acceleration

Identify the most expensive lines of code that affect the speed of a model code during training and prediction.

Model code-level acceleration

Optimise the prediction speed of your model code by leveraging more efficient runtimes (such as Pytorch, ONNX, etc) for your hardware (such as GPU).

Distributed Computation

Leverage the distributed architecture of evoML to optimise speed by parallelising the most performant critical components.

How evolutionary code optimisation works on
ML model code?

Inspired by Darwin’s theory of evolution, we use evolutionary algorithms, meta-learning and search-based software engineering, to provide the best version of model code for lower latency and higher throughput as well as less memory and energy usage.

Sign up for your free trial!

Our team will guide you through a demo of how you can achieve optimal model code and accelerate implementation with evoML.

Why TurinTech?

Cutting-edge Research Built into Product

TurinTech is a research-driven company, with over 10 years of experience in the code optimisation area, and backed by leading investors.

Trusting Partners from Day One

Our expert researchers, data scientists, and software engineers will work closely with your team, building the roadmap to AI success for your business.

Future-proof your Business

We are future-oriented, constantly developing technology for today and tomorrow. Working with us helps you future-proof your business.

Tailored Solutions for your Business

We understand that each business has unique needs and goals. That’s why our team will work closely with you to  develop personalized solutions that meet your business needs.