How democratised Machine Learning can transform your business
Let’s dispel a misconception. Artificial intelligence (AI) and machine learning (ML) are often seen as beyond the reach of businesses without data scientists and ML engineers.
Let’s dispel a misconception. Artificial intelligence (AI) and machine learning (ML) are often seen as beyond the reach of businesses without data scientists and ML engineers. As we explored in chapter six of AWS eBook Innovate With AI/ML To Transform Your Business, this is no longer the case. AI has been ‘democratised’ — a process of becoming accessible to people at all levels of experience and skill — and it’s opening up opportunities for every business.
Here, we’ll cover two main ways you’ll see ML democratisation in the real world: accessible education and simplified technology. We’ll then look at one of the best flow-on effects of democratisation: lower costs of using AI and ML.
AI education for all
In our recent SmartCompany/AWS seminar, Simon Johnston, AWS Artificial Intelligence and Machine Learning Practice Lead for ANZ, had this to say: “Empowering other people with the technology is part of our mission statement at AWS. We want machine learning in the hands of everyone.” For AWS and other players, the real benefit of AI comes from opening the door to all, and education is key to achieving that goal.
Education doesn’t mean years of highly specialised data science training. Rather, this is foundational learning that’s designed to introduce AI and provide basic skills. As we’ll see in the next paragraph, AI technology is becoming more accessible, so education doesn’t need to be as rigorous. One accessible pathway is AWS Machine Learning University. With these courses, anyone can learn the basics of ML, progress at their own pace and choose the appropriate education for their needs.
At the absolute entry-level, options like Machine Learning Essentials For Business provide some guidelines for how ML might work within your business, even if you’re just beginning to explore it. Other low-barrier tools like AWSome Day Online Conference and Builders Online offer some basics to cloud-computing that can ease the transition into a more thorough knowledge of ML. Regardless of where you start, education is going to play an increasingly important role in bringing AI into the mainstream.
Lowering the barrier to entry
The advanced mathematical, statistical and programming knowledge that has underpinned ML is still vital, but it’s no longer a prerequisite. Even as education becomes more accessible, using AI and ML is becoming much simpler. In chapter four of Innovate With AI/ML To Transform Your Business, we spoke about ML-powered use cases — functions like intelligent document processing and call centre automation that are approachable to businesses without traditional AI expertise. But it goes further.
Tools like Sagemaker Canvas are no-code ML options. Beyond just using end products (as in ML-powered use cases), there is potential to create custom models and start making ML-guided predictions without having to write a single line of code. Business analysts, as Simon Johnston points out, are able to complement their in-depth knowledge with ML, planning inventory, predicting customer churn, optimising revenue and more. “Business analysts know their part of the business really well, know their data and use cases but don’t know machine learning,” Johnston says. “They shouldn’t be prevented from developing these capabilities. That’s what Canvas allows.”
A cost-effective solution
One aspect of ML democratisation that shouldn’t be forgotten is how it affects cost. Traditional models of AI and ML rely on data scientists to build and train machine learning models. According to SEEK, data scientists earn an average of around $115,000 per year, making ML a costly investment — particularly when it comes to scaling.
To use an example from the SmartCompany/AWS seminar, Augustinus Nalwan, GM of AI, Data Science and Data Platform at Carsales, ran into this exact issue. As the business focused more on AI to solve problems, the demand for data science skills increased. Rather than hire, though, Nalwan used Amazon Sagemaker to allow other members of the Carsales team to assist at a significantly reduced cost. “At Carsales, 70% of AI models can be built using this platform’s algorithm which does not require data scientists,” Nalwan says. “Anyone with good practice and guided by data scientists can perform this job.”
Learn about the 6 key trends driving Machine Learning innovation across Australian and New Zealand industries inclusive of improvements to Model Sophistication, Data Growth, ML Industrialisation, ML Powered Use Cases, Responsible AI and ML democratisation.
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Read now: The real-world ways that businesses can harness ML
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