Mastering machine learning (ML) isn’t easy.

For small and medium-sized businesses, it takes considerable time to grasp this branch of artificial intelligence and even more to use it effectively in solving business problems. Lack of proper infrastructure to run ML models, inability to pick the right algorithm, and data science talent shortage are a few of the reasons for this.

Trying to overcome these obstacles, one by one, may not be a cost-effective approach for small and medium-sized businesses.

Enter low-code and no-code machine learning platforms.

Making machine learning accessible

Low-code and no-code machine learning platforms enable businesses to apply machine learning without extensive knowledge and training in the domain. These tools empower citizen developers—individuals without formal training in software development who uses no-code and low-code platforms – to create machine learning applications and reduce the burden on data scientists.

More precisely, they enable smaller companies to taste machine learning and larger companies to free up their data scientists so that they can work on more complex projects.

For example, Obviously AI lets users make data predictions without writing any code, Clarifai is useful to transform unstructured data into actionable insights, and MakeML allows users to create object detection and segmentation models without writing any code.

Similar to how no-code development and low-code development platforms are used to develop software applications quickly without coding and with minimal coding respectively, no-code and low-code machine learning solutions help build and train ML models with ease.

It’s safe to say that low-code and no-code machine learning tools aim to democratize artificial intelligence and reduce the entry barrier. Such tools have already started disrupting the machine learning space and are convincing more businesses to utilize ML.

The Data Science and Machine Learning Platforms category in G2 lists such tools that allow users to build, deploy, and monitor ML algorithms. Some of these platforms come with drag and drop interfaces meant for novice users, while others are meant for users with coding expertise.

The difference between traditional and no-code machine learning development.

Source: towardsdatascience.com

No-code platforms are typically not flexible enough due to restrictions on modifying or accessing backend code. This also means that these tools are more suitable for non-programmers who don’t know programming languages like Python or R. Users can upload the relevant data, click on a couple of buttons, and the tools will build a model. 

For example, such a tool can optimize operations for better efficiency, find ways to improve customer experience and reduce churn rate, or price products the right way.

A key feature of these platforms is the ability to automatically perform machine learning model selection and training. The platform would select and employ the algorithm or approach that suits a particular problem the most. Additionally, it also analyzes model performance with time and the introduction of new data, and optimizes its function accordingly.

AutoML vs. no-code AI tools

Automated machine learning (AutoML) tools automate the manual and monotonous tasks that data scientists must perform to build and train machine learning models. Feature selection and engineering, algorithm selection, and hyperparameter optimization are examples of such tasks.

It’s natural to confuse AutoML tools with no-code AI solutions. Although they might eventually merge and become a single category, currently, they have different characteristics.

While no-code machine learning platforms enable non-technical users to build machine learning models, most AutoML solutions aim to empower data scientists to become more efficient. They also provide better transparency in the entire machine learning pipeline and help data scientists refine how machine learning models are built.

Low-code machine learning tools help predict churn rates, create simple image recognition models, optimize workflows, and create recommendation systems across several industries. They can significantly accelerate the model development process with project templates and readymade datasets.

For example, Microsoft’s AI Builder lets users effortlessly create and manage machine learning models to process text, predict business outcomes, and analyze customer sentiment. Viso.ai is another platform that’s useful for developing computer vision applications. Such tools are expected to be extremely helpful for product development, marketing, branding, customer service, and more.

Low-code platforms empower non-technical people to find solutions to low-end problems without relying on data scientists. Along with reducing the reliance on data scientists, non-technical employees also get a chance to understand how exactly data impacts their decisions.

For example, marketers can use such tools to predict churn rates or quickly understand the current market climate. This will allow them to make rapid decisions based on data and stay up to date. Marketers can also use low-code automation tools to configure a website’s chatbot with a natural language processing (NLP)-based approach. For example, the tool can help identify commonly asked questions and prime the chatbot to take proactive steps.

By 2030, the low-code development platform is expected to generate a revenue of $187 billion. The platform's growth rests on its ability to manipulate part of the code, unlike no-code tools, giving low-code a better scope of customization as per business requirements.

If no-code and low-code AI tools are so useful, then what are the issues?

Although no-code and low-code machine learning tools are helpful to eliminate (or reduce) the entry barrier of AI and machine learning, they come with their limitations:

At the time of writing, these tools don’t have the flexibility or maintainability of traditional machine learning applications. Hence, businesses must have a clear understanding and vision of what problems to tackle using these tools. If they wish to create a proof of concept (POC), then no-code tools are ideal. But if they aspire to develop scalable solutions, then the traditional machine learning approach would be a better way forward.

Minimum requirement: knowledge of no-code machine learning platforms

At G2, we believe that in a few years, the knowledge of no-code machine learning platforms will be considered as a minimum requirement for most jobs. This will be especially true for product managers and job roles that have to deal with data daily.

We also expect to see several of these tools evolve to become industry based. For example, there may be a no-code AI platform to solve marketing-related problems or a tool to solve any problems in the manufacturing industry.

Currently, no-code and low-code machine learning platforms are widely used to create quick POCs. This helps empower individuals in non-technical roles to showcase their ideas to data scientists and evaluate whether they are feasible. And if you’re wondering whether these tools will replace data scientists, the answer is a big “no”. Just like any other AI-related technology, no-code machine learning platforms are meant to take over monotonous tasks and help individuals skip a few steps.

The better question would be, “How much of a data scientist’s work could be automated using a no-code machine learning platform?”