Machine learning (ML) drives insights and optimizes decisions across domains. The adoption of ML tools, however, remains bottlenecked by the shortage of experts capable of building and deploying complex models. Fortunately, tools are available that place sophisticated ML capabilities into the hands of non-technical practitioners, democratizing the technology and empowering individuals and organizations. Intuitive visual interfaces, automation, and pre-built templates allow business users with limited data science expertise to train, evaluate, and utilize ML models- all without writing a single line of code. Examples demonstrate that these no-code systems facilitate use cases like predictive analytics, document classification, object detection, and natural language processing. By empowering domain experts directly, no-code ML tools have the potential to accelerate and spread the benefits of ML to more decision-makers.
These ML models, with artificial intelligence (AI), have transformed businesses, allowing them to streamline operations. Thanks to these tools’ data-driven insights, no-code ML models can analyze vast data sets to uncover patterns and trends, allowing businesses to make informed decisions. For example, Netflix uses ML to recommend movies and TV shows to users, which boosts customer engagement. AI also powers robots and chatbots that help automate repetitive tasks, freeing up human employees for more strategic work. Marketers can leverage no-code ML to evaluate sales leads and predict which have the highest conversion potential. Finance departments can use no-code ML tools to predict revenue growth or evaluate the credit risk of a new customer. In logistics, analysts can use ML models that identify optimal shipping routes based on a variety of factors. According to a 2020 study by McKinsey, automation could generate up to $3 trillion in added value in 2030.
The Shortage Problem
Unfortunately, while many companies are willing and eager to embrace these new models, the distinct shortage of experts capable of building and deploying them remains an issue. This is partly because the extremely high demand for these skilled professionals far exceeds the current talent pool, given this is a relatively new field. Another issue hampering advances is that there aren’t enough college graduates with the proper training. The skills required to build complex ML models include expertise in statistics, programming, domain knowledge, and communications, which go beyond those many current software developers possess.
The No-Code Workaround
Companies can address talent shortage by utilizing no-code ML tools, which feature drag-and-drop interfaces and pre-built models that allow users with minimal coding experience to build and deploy ML models. These tools help to streamline tasks such as data preparation, model selection, and training, making ML more accessible to “citizen data scientists” and empowering non-technical teams to leverage ML insights for their specific needs. Naturally, there are some details that users who are not ML experts need to know before diving in, including understanding the company’s business problem and data landscape and preparing and organizing the requisite data effectively to establish the model training.
One tool that can help with this is Amazon SageMaker Canvas, which does not require coding skills and democratizes ML by enabling non-technical users to build ML models. This can free up data scientists for more complex tasks. It also helps develop the models faster with its drag-and-drop interface and pre-built models.
Beyond the Data
Of course, it’s simply not enough to build the models. Companies must know how to interpret and communicate the results with the relevant stakeholders. As such, it’s worthwhile to invest in, recruit, train, and upskill staff to deploy and optimize AI and machine learning initiatives effectively. This can be achieved by hiring data scientists and focusing on diverse talent, such as domain experts and business analysts.
Other avenues open to companies include providing internal training programs, partnering with universities to upskill current employees, and encouraging cross-functional teams to work together on AI projects. When the COVID-19 pandemic upended its usual prediction models for memory chip demands, Samsung Electronics started using Amazon SageMaker Canvas in August 2022. A month later, the company’s business analysts used the product to analyze data and forecast demand for PC shipments for the next eight quarters. With its user-friendly interface, “even a business analyst like me can analyze data and get insights using machine learning,” said Samsung Electronics’ Manager of Market Intelligence Dooyong Lee.
The advent and development of no-code are democratizing AI and ML. This boon makes previous new-fangled and complex products accessible to non-technical users while boosting innovation and efficiency within companies. Nonetheless, for it to be successful, collaboration is critical, which can be achieved by utilizing teams that possess domain expertise and technical skills. It’s imperative to stay on top of this fast-moving technology by investing in continuous learning to ensure practitioners continue to upskill and stay updated with AI advancements.
Navigating the Pitfalls
When using any type of ML tool, companies need to be mindful of potential ethical concerns regarding algorithm bias and the lack of human oversight in automated decision-making. This is especially important if no-code tools are used to build ML applications without understanding the underlying data or the statistical probability of spurious correlations.
Data security and privacy issues also need to be considered. Users mustn’t be able to receive answers that could potentially expose data they are not authorized to see. For example, while it may be okay to ask what the average salary is of employees at a particular company, asking for the specific salary of an individual should not be permissible.
There is a risk of job elimination when machines are used to perform mundane tasks or additional skills are needed by current employees. This can be mitigated by freeing employees to take on new tasks once their everyday tasks are automated and offering employees the opportunity to undertake further training.
Embracing the Future
While it takes time and money for companies to train and upskill workers, the beauty of no-code is that it opens a whole new world of machine learning and AI possibilities for all—including small businesses and non-technical individuals. With the proper training and mindset, opportunities will continue to expand for those whom Gartner refers to as “citizen developers,” allowing companies to harness and embrace the endless possibilities of ML and AI.
