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 […]
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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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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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, […]
Data scientists often use Feature Selection techniques to reduce the number of features and keep the most relevant/useful ones before training a ML model on data. It can improve data quality, and help the ML model to focus on the most relevant information in the data, thus improving the efficiency and effectiveness of the training. […]
AI-driven Data Catalog
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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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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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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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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 […]