How do you get started with machine learning and not break the bank? There are a few basic problems smaller companies and start-ups will encounter.
In 2020, AI has become commonplace in the majority of large tech companies, but what about smaller ones?
A prediction from Forbes states global Artificial Intelligence (AI) software revenue will reach $126.0B by 2025 (growing from $10.1B in 2018). Within this market there has been significant advances in the exciting dimension of machine learning (ML). This technique uses data inputs and turns them into predictions, and, akin to a particularly human trait, these algorithms improve automatically through experience.
ML can be applied to improve various aspects of a business, like the automation of processes, improving customer experiences and enabling real-time decision making just to name a few. ML in some form is nearing ubiquity in larger companies and tech giants, integrated seamlessly into the products you use everyday. So, how do you get started with machine learning if you are a smaller business or start up - and not break the bank? There are a few basic problems a startup will encounter:
Building a team of machine learning engineers is expensive.
- You need machine learning engineers to integrate machine learning into your technology. Talent is scarce and a costly investment. Programmers are already quite pricey. Add machine learning experience and suddenly the competitive rates double.
- If building an in-house team is beyond your budget, you can outsource the problem as a cheaper alternative. But you need to find freelancers, build trust, and, even then, third parties might not deliver.
Someone needs to label data.
- Machine learning algorithms require tons of data and, in many cases, the data needs to be labeled. This process requires someone to sit down and sift through photos, for example, and mark them. Most companies can’t afford having their engineers clicking through photos and labeling them as “cat” or “non-cat” and choose to outsource the process.
- However, outsourcing runs into many problems with accuracy, which is vital for machine learning algorithms to run smoothly. Another solution is to build an in-house data operations team. This also requires a lot of time and can be pretty inefficient, both of which mean squandering funds.
The potential effects of machine learning aren’t well-understood.
- Machine learning has become integrated into our daily lives in ways we can only begin to become aware. These algorithms have consequences. For instance, the addictive nature of YouTube’s algorithm can lead to much more than a night of binging cat videos.
- People do not trust algorithmic decision-making. In the UK, the A levels - an end of high school exam that determines your college admission - were cancelled due to coronavirus and replaced by an algorithmic decision. To the dismay of many young students, the algorithm returned many results that were lower than expected. The country responded with protests and anger, resulting in the dismissal of the top civil servant at the Department for Education and the replacement of algorithmic results with suggested grades from teachers.
Traditionally, taking on the task of building your own AI and ML capability is a large commitment of resources and responsibility.
So, should you use ML in your business?
In the past several years, machine learning has evolved from a field of study to a invaluable business instrument - At PI.EXCHANGE we strive to make machine learning more and more accessible to a broader audience and businesses of various sizes, allowing small businesses to compete with larger companies.
Designed to keep start-up and operational cost low, The AI & Analytics Engine can guide you to build and manage ML applications whether your business objective is to increase sales, improve customer experiences, automate manual tasks that drain resources, or generate data-driven predictions in real-time (to name a few!).
The AI and Analytics Engine was created with startups and small businesses in mind. With the potential value of implementing ML into the core product offer or to improve business processes increasing, and with tech giants afforded a clear a head start, there is no better time than the present to get started with ML.
But don't take our word for it. Try it yourself. Built and tested by machine learning engineers, we want hear how we can make our Engine even more efficient.