Increasingly more organisations are adopting AI solutions to refine decision-making processes. From understanding consumer sentiment to preventing financial fraud, the applications of AI are multi-faceted. However, building robust AI solutions can be costly, financially and otherwise.
At TurinTech, we believe developing and scaling AI is all about optimising four crucial elements in your AI ecosystem.
1. Technology: Simplify
Technical complexity is one of the biggest challenges in developing and scaling AI.
In the white paper Scalable AI, the Software Engineering Institute of the Carnegie Mellon Institute note: “two factors underlie the renewed focus and promise of AI over the past decade: 1) the availability of large amounts of data and 2) computing resources that can support the data processing and computational demands of modern AI techniques.”
However, the mere availability of large amounts of data and computing resources does not simplify the process of developing and scaling AI systems. Building AI solutions still require intensive computing capacity. Putting technical infrastructure in place come at a financial cost that most companies cannot afford. According to the blog article How much does artificial intelligence cost? Well, it depends by Itrex, “you may easily spend $50,000 on a very basic version of the system you’re looking to build”. Therefore, to harness the true potential of AI, organisations require simpler and cost-effective technological implementations of AI systems. This would mean simplifying the end-to-end process of developing AI-based solutions.
Moreover, the use of AI is no longer confined to a desk-based computing setup. Especially with advancements in the Internet of Things (IoT), AI needs to adapt to work across a range of application scenarios. For example, the Appinventive article 14 Ways IoT is Impacting the Food & Agriculture Industry highlights numerous ways in which different smart devices are being used in the food and agriculture industry. These use cases emphasise the importance of building AI that can easily and accurately work across a variety of devices and unconventional environments.
2. Time: Minimise
Manually executing a machine learning pipeline can be extremely time consuming. According to Algorithmia 2020 State of Enterprise Machine Learning report, companies can take from 8 to 90 or more days to deploy a single machine learning model . In the time it takes to develop and deploy a model, the conditions of the market can change, putting insights driven via AI solutions at risk of being Overcome By Events (OBE).
In order to meet industry demands while staying relevant and accurate, AI solutions need to minimise the time taken from conception to deployment. This enables organisations to derive faster insights from their data, allowing key decisions to be made efficiently.
An important aspect of minimising time also ties back into utilising simpler but cutting-edge technology solutions for AI. State-of the-art technical solutions can reduce execution time of trials, saving valuable time.
3. Talent: Diversify
Creating custom AI solutions for an organisation requires experts with significant domain knowledge and experience. Given the training required, AI experts are scarce and in high demand. The LinkedIn 2020 Emerging Jobs Report UK mention Artificial Intelligence Specialist as UK’s number one emerging job, showcasing the demand for AI experts. Given this demand for talent and the scarcity of experts, organisations may have to pay significant amounts to secure the services of AI specialists. Hiring experts further drives up the overall financial costs of implementing AI systems.
An innovative solution would be to provide anyone making vital business decisions in the organisation the opportunity to work with AI. In order to diversify the set of individuals that uses AI, organisations can utilise non-specialist tools which can easily be used by data science and business teams.
By implementing solutions that can be executed with a few clicks, even those with little background in AI still get to contribute to the business decision-making process. This is also an essential step in democratising AI; qualifying more individuals to use AI creates the space for generating novel and more inclusive decisions.
4. Transparency: Amplify
Organisations entrust AI with making sensitive decisions. This is particularly the case in sectors such as healthcare, law, and finance. Organisations have to be able to explain the underlying assumptions and mechanisms used by AI in making these crucial decisions. Companies may also need to explain their AI-based decision-making processes to regulators and other stakeholders.
However, making AI transparent is not only about meeting external demands and expectations. Organisations have a responsibility to ensure that the AI solutions they implement are fair and ethical, so that no individual suffers from biased decision-making. This calls for an increased need to build AI solutions with greater transparency. To scale and use AI in a range of scenarios, companies need to explore ways in which their AI systems can be made explainable.
These are four critical elements in ensuring that organisations build and scale AI effectively to derive timely and accurate business insights. However, going through each of these tasks seem like a cumbersome process in itself. So what’s the next best step to take?
We recommend you take a look at evoML.
evoML is a state-of-the-art AI optimisation platform developed by TurinTech. Backed with 10 years of research expertise, evoML automates the end-to-end data science cycle and brings the entire machine learning process into a single platform. evoML carries out the model-building process at an expedited rate, saving you precious time. You are also able to deploy the model with just a few clicks.
With embedded code optimisation, evoML ensures that AI solutions adapt to the demands of your business problems, from accuracy to explainability to other user-defined metrics. The platform is code-free and straightforward, allowing anyone in your organisation to incorporate AI into the decision-making process. evoML provides you visualisations, explanations, and underlying model code on the machine learning process, making it transparent and explainable.
For more information about building efficient and scalable AI, check out our blog Optimising AI with Multiple Objectives.
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.