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

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 faster than ever before. It helps companies stay ahead of their competitors and continuously increase their revenue.

As an exclusive guest on the DataXpresso podcast, our Co-founder and CEO, Dr Leslie Kanthan, discussed all-things AI and why organisations cannot afford to overlook code optimisation.

 

Find out what was discussed below:

 

What’s the importance of getting the right database in place?

There are so many companies who have a lot of what we term as ‘bad data’ or what others may see as ‘dirty data’. This means that it’s incomplete. It’s siloed in different areas of the business, for privacy issues, is mislabelled, unstructured, etc. Training an AI model on these so-called ‘data swamps’ is very difficult and tends to produce very, very poor results.

Well sourced, consistent and labelled data is key to a successful AI transformation. Furthermore, in an increasingly data-driven world, organisations need to process data and make optimal data-driven decisions in real-time.

However, this can’t be done without fast data infrastructure and fast AI. That’s why having the right database in place centralises and processes this kind of enterprise data from multiple sources, which can allow companies to access AI-ready data and build efficient models.

 

Why is access to data such a big challenge?

Access to data goes hand in hand with ease of use. It’s a means to obtain routes to interpret, review, and analyse data that is typically siloed or unreachable to many different people, ranging from data scientists to analysts.

Being able to access a steady stream of data and perform your models or analyse them is incredibly important which is an ongoing issue that’s now being addressed. What we’re also seeing is different ways of storing data to ingest, interpret, analyse and support organisations further.

 

What should organisations bear in mind when they’re implementing AI properly and effectively?

Machine learning has enormous potential in business applications, but like most companies, they are still very much at the earliest stages of its adoption. This is only going to accelerate, and there is going to be exponential growth for ML business adoptions when core ML development platforms and tools become more available.

In addition, explainable AI – which seems to be like the biggest thing right now – will enable employees to trust AI more and use it as a decision making partner.

 

What are the benefits of your AI Optimisation platform?

The first positive of AI optimisation is that it allows organisations to capitalise on opportunities better and faster, stay ahead of their competitors and increase their revenue streams. The second positive is the code-free process that allows non-coders to run projects on their own, reduce reliance on other teams, and alleviate pressures on overburdened teams or recruiting new hires.

In addition, you can have numerous projects simultaneously running and increase productivity as well as freeing up people to focus on more important tasks. Users across these different departments can also access areas to collaborate and share their knowledge.

 

What advice would you give to executives that want to maximise the ROI of their AI efforts?

When we look at AI optimisation, one of the first priorities is to be AI-ready, and once they are, they can move on to being AI efficient. That said, how do you build AI at scale with efficiency?

It means an entire architectural chain should be improved using advanced analytical databases and the latest AI tools that work in conjunction with those databases. With market opportunities and the risk of lagging behind the fierce competition, organisations will need to develop the models at scale to drive advanced analytics, profitability and stay ahead. 

 

How does sustainability fit into this?

Corporations are now recognising that there is a moral responsibility to be sustainable and reducing their carbon emissions is now a big part of the business. A lot of what we do is optimisation on underlying code to improve code efficiency and reduce energy consumption. 

If we think about large tech companies, running data centre servers, powerful GPUs and computational processing power, the list is endless. They’re using a huge amount of energy that can be very taxing for the businesses economy. What we’re seeing is that there is a big push to ensure that energy is reduced to a minimum and only the absolute necessities are required – which will naturally reduce the cost.

 

To find out more, listen to the full podcast episode here. To stay up to date and follow our progress, check out our LinkedIn and Twitter.