From AI-ready to AI-efficient: TurinTech joins the DataXpresso podcast

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AI Insights, Insights

With digitalisation accelerating, more and more companies are adopting AI to maximise their company value. In recent years, businesses have realised that they need to actively scale AI efficiently or risk being left behind. In the era of all things digital innovative technologies like AI optimisation, is empowering organisations to capitalise on opportunities better and […]

Legislate meets TurinTech: How AI is improving sustainability and making a difference in the Legal sector

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AI Insights, Insights

TurinTech Co-Founder and CEO Dr. Leslie Kanthan recently joined the Legislate Podcast  to discuss all things AI with podcast host Charles Brecque. Leslie dove into how TurinTech’s evoML platform is making machine learning models more sustainable, future plans of the company, and how AI technology can help make the legal processes more efficient.  Continue reading […]

How to empower data scientists

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AI Insights, Insights

Artificial intelligence and machine learning have long been acknowledged by business leaders as a means to leverage business growth, from valuable customer insights to operational efficiency.  Yet it can be difficult to scale, and, despite cutting-edge research, data scientists are still struggling to fully utilise AI and ML. With this in mind, we have put […]

How Can Complex Models Run Fast? Trade-off between model complexity and running speed

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Insights, Optimisation Lab

There are several concerns when selecting machine learning models for a specific task. The model performance is of course a prime concern. Besides, the model complexity and speed are vital in scenarios requiring a fast deployment or quick response from models. In this article, we discuss the reasons behind fast and complex models, and how […]

Fast Hyperparameter Tuning to Improve Model Performance

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Insights, Optimisation Lab

Image by Daniel J. TOTH Hyperparameter tuning plays an important role in the process of training an optimal machine learning model. During the training process, the performance of the target model is evaluated by monitoring metrics such as the values of the loss function or the accuracy score on the test/validation set, on which basis […]

TurinTech’s evoML reduces AI’s carbon emissions by 50% with multi-objective optimisation

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Insights, TurinTech News

London, UK; 12th January 2022: TurinTech, the UK company which empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle, has announced its greener AI platform- evoML- which reduces AI’s carbon emissions by 50%. With the average carbon footprint of AI equivalent to five times the lifetime emissions of an […]

What Is Imbalanced Data and How to Handle It?

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Insights, Optimisation Lab

Imbalanced data is a common problem in machine learning, which brings challenges to feature correlation, class separation and evaluation, and results in poor model performance. In this article, we will introduce: Imbalanced data in practice Three challenges with imbalanced data Top 6 data sampling techniques Sampling techniques comparison Python code snippets Imbalanced data in practice […]

Four Methods to Statistically Measure Your Data Correlation

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Insights, Optimisation Lab

Correlations is a measure of the association between variables. They measure to what extent one variable is affected by a change in another variable. In this article, we will explain the importance of data correlation in machine learning, and introduce four common methods to calculate your data correlation. Why Data Correlation is Important? Understanding which […]

Feature Generation: what it is and how to do it?

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Insights, Optimisation Lab

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, […]

AI-driven Data Catalog

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Insights, Technology, Use Cases

Problem Data Catalog is an inventory for datasets. It provides detailed data description (i.e. metadata), such as when it was created, to enable users find and understand a relevant dataset. For many traders in financial services, data catalog enables their investment decision making to be more data-driven and evidence-based. Take Environment, Social and Governance (ESG) […]

AI-enhanced Data Quality

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Insights, Technology, Use Cases

Problem According to Gartner, $14.2M is lost to poor data quality each year. Data quality refers to how suitable the data is for its intended purpose. High quality data is extremely important as it impacts the performance of business operations. For financial services, in particular trading teams, data must be of extremely high standard, as […]

AI-powered Data Governance

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Insights, Technology, Use Cases

Problem Data governance is prominent in helping financial institutions stay compliant as digitalisation accelerates. It ensures that an institution has the right data available at the right time, and that the data is accurate and in the required format to satisfy specific business needs. One of the most critical component of data governance is Know […]

Automated Data Mapping​

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Insights, Technology, Use Cases

Problem Since data resides in various systems, data mapping helps bridge the gap between two systems, so that when data is moved from a source, it remains highly accurate and usable at the destination. For any financial institution, operational risk management is critical. Prior to analysing operational risk data, organisations must map data to homogenise […]