The popularity of AI and machine learning (ML) has skyrocketed in recent years. In fact, STX Next’s 2021 CTO report found that around two-thirds of businesses have now implemented ML in some form and a further three-quarters of chief technology officers see it as the most likely technology to come to prominence in the next two years.
Maximising the potential of ML means being fully aware of the complexity and unique challenges that come with these types of projects, and taking the necessary steps to overcome them.
For businesses operating on a global scale, the volume of structured and unstructured data created every day has grown exponentially in recent years, in large part due to the increased usage of technology to support business. For organisations and end users, AI and ML-powered products can assist in processing it, thus streamlining operations, eliminating human error and easing the process of international expansion.
So what are the challenges that can be encountered in a typical ML project and how best can technical teams combat them?
Understanding the business goal of ML and communicating it well
Before coding begins, it is vital to have a detailed understanding of what is desired by the client and be able to communicate that to the rest of the team, who might not always be present at every meeting.
This means understanding a business’s purpose, budgets and being able to recommend the best course of action. There could be a simple solution to a client’s problem that takes a project in a completely new direction. At the same time, budget constraints could mean that you need to cut corners in some areas and prioritise others.
For a product to reach a global audience, it is important to understand how it may need to be tailored to fit the needs of different regions. This should be carefully considered and planned out before the development process begins.
Data availability, bad data and bias in data
When organisations are planning global expansion, data sets can be vast, complex and diverse. ML is then crucial to organising this information and accurately aligning data with the relevant markets so that it can be used in a way that will add value to the business.
The reality is, when a business first begins working with customers, the right data is often not available or in the wrong format. Collecting and annotating data is a very expensive process, and we rely on data engineers to put this right. This is why it is so crucial to prepare data accordingly so that when the moment for expansion arrives, businesses are equipped with all the information needed to make the right decisions.
Performance management is key to meeting ML challenges
When deploying a model to production, performance matters. Once deployed, technical teams should take time to measure and test performance, and use this data to make any necessary improvements and changes.
If a product is deployed internationally, monitoring should be localised so that performance can be reviewed across different regions. Occasionally businesses will have to prioritise speed or accuracy, but this can be managed according to the judgement of team members, clients and relevant experts.
By automating processes and improving efficiency, ML frees up businesses to take on more projects and extend operations into new markets. Getting to grips with this tech is certainly a challenge but one that can facilitate global expansion if the right amount of attention is given to mastering and maximising its potential.