AI Insights, Insights

In a previous article, How to achieve optimal performance: code optimisation in the AI ecosystem, we looked at the value of code optimisation in the AI ecosystem and the ways in which machine learning model code can be optimised. Code optimisation is critical in improving the performance of machine learning models and AI solutions. Prediction speed is one critical performance metric that code optimisation can improve, and in the fast-changing business world, speed is money. This article discusses four instances where machine learning model speed can be critical for businesses, and the role code optimisation plays in achieving this.


Capitalising on profitable trading opportunities before competitors
Due to the ability to analyse a large and varied body of information, machine learning-based models are used in trading to make more accurate and swift decisions. Our article Artificial intelligence for hedge funds: How can machine learning and code optimisation generate greater alpha? discussed in detail useful applications of AI in trading. With optimisation, machine learning models are able to perform quicker, generating trading decisions at a faster rate. It is reported that an advantage of one millisecond can be worth $100 million a year in trading. This emphasises the value of speed in the machine learning model.

Improving customer experience for lower churn
Most consumers now default to using digital channels to engage with a business. While web-based services and mobile apps can make a business and its products and services more appealing to customers, sub-par digital support can also turn customers away easily. Research by has shown that around a 30% increase in latency costs more than 0.5% in conversion rate. A drop in conversion leads to reduced revenue and can translate to millions lost in profit. For a user to feel that a system is reacting instantaneously, the system must ideally display a response time of 0.1 seconds. Code and model optimisation ensures that AI applications meet these ideal performance rates, providing users with a seamless experience, leading to greater customer retention and lower churn rates.

Life-critical decision-making
Emerging AI applications for autonomous vehicles and healthcare are not only novel but they also make life-critical decisions. In a recently launched project, In the Moment (ITM), the US Defense Advanced Research Projects Agency (DARPA) is aiming to develop AI systems that make critical decisions in environments that are rapidly evolving, uncertain, and have no “ground truth”. For example, in medical triaging, due to limitations in resources, medical professionals are required to decide which emergency care patients to prioritise, based on the fatality of their illness or injury. AI solutions used in such critical situations must display robust results at speed.
Latency in systems that make decisions in real-time can cause serious harm and severely jeopardise the credibility of the system. In 2016, a Tesla on autopilot crashed into a white truck, failing to distinguish the vehicle due to its colour. The collision led to much hesitation about the safety of self-driving vehicles. Such incidents reiterate the importance of machine learning model speed and accuracy in AI systems. Optimised machine learning models are able to reduce inefficiencies in life-critical AI applications, allowing for more accurate decisions to be made faster.

Improving the performance of edge devices
Edge devices are becoming commonplace across industries, with applications such as security cameras, drones, and wearable tech. Edge devices and the Internet of Things (IoT) allow greater capacity for scalability, accessibility, and speed, making them extremely useful for businesses. Increasingly, more analytics functions take place within these devices, particularly to reduce latency in decision-making. For instance, data can be processed within an edge-device itself to mitigate risks arising from reduced network capacity. However, since edge devices usually have memory and power constraints, code optimisation can be used to improve machine learning model performance to suit the requirements of the targeted devices. This will lead to timely and accurate analyses, allowing businesses to make the best use of edge AI.


In order to explore the potential of code optimisation and its industrial applications, TurinTech has conducted extensive research in the field. Our team of experts have over 10 years of experience in researching code optimisation. TurinTech studies have shown that code optimisation can lead up to 46% improvement in execution time, 44.9% improvement in memory consumption and 49.7% improvement in CPU usage, leading to greater speed in machine learning models, and overall better performance of AI applications. A key research application of TurinTech is its evoML platform, which incorporates multi-objective optimisation and code optimisation into the data science process. With evoML, businesses are able to easily build fast and efficient machine learning models, leveraging the power of AI to boost profits.


About the Author

Malithi Alahapperuma ​| TurinTech Technical Writer

Researcher, writer and teacher. Curious about the things that happen at the intersection of technology and the humanities. Enjoys reading, cooking, and exploring new cities.