Introduction
In 2024, almost everything you see has been automated or is on the verge of undergoing the same, which makes it all the more important to introduce you to ‘No Code ML’. From sending an email to backing up files, scheduling social media posts, or even sending email reminders, machines have revolutionized how humans perceive “working.” Most of this paradigm shift, from manually doing these tasks to making machines do them, is because of machine learning (ML) and artificial intelligence (AI), whose impact on our lives is only growing.
When these technologies, ML and AI, started emerging, it was realized early on that while they carried the vast potential to transform various businesses, they would be a vigorous area of work for non-technical people.
To expand their applicability for those who cannot work technically to build them, no code or low code machine learning came up after a few decades of ML being limited to tech experts.
This was the starting point for the No Code ML, or simply DIY ML deployment, to work towards a world where every machine learning task is available without the pain of extensive coding.
This blog will familiarize you with both while illuminating the widespread debate “No Code vs. Traditional Machine Learning.”
Table of Contents
No Code Machine Learning
There is a common misconception about machine learning that necessitates one to be a coding expert in utilizing machine learning algorithms in workflows/projects. Fortunately, that is no longer a case in point. Due to the recently rising utility and applicability of ML across all industries and businesses, several tools and platforms allow anyone to build machine learning models without any coding requirements. They allow business experts to experiment and test their theories without the need for AI/ML expertise or even a conscious awareness that they are “doing AI or ML.”
What is No Code Machine Learning?
Source: National Informatics Centre
No Code/low code machine learning is an alternative route to using machine learning models without going through the coding part. There are various tools and platforms, like Google AutoML, Microsoft Lobe, Data Robot, etc., that allow you to build ML models conveniently. With these platforms, you just have to upload the data, choose the kind of model, and see the platform do the rest.
Source: Obviously AI
Examples of No Code Machine Learning tools
There are numerous promising No Code machine learning tools. Some of them are-
1. Google AutoML
This no-code platform from the world-famous tech giant Google allows users with no-to-minimal machine learning experience to train high-grade machine learning models customized to their business requirements.
Source: 9to5 Google
2. RunwayML
RunwayML is a platform that enables creators to use machine learning capabilities for media types ranging from text to audio to video without the knowledge of programming languages. They can develop and utilize pre-trained models for producing photorealistic photos or image descriptions.
Source: Runway
3. CreateML
If you are interested in iOS development, Apple’s CreateML is one of the best no-code ML platforms. It comes as a standalone macOS program with pre-trained templates that take images, videos, tabular data, and words as input. Using this input, it creates classifiers and recommender systems.
Source: CreateML
4. Data Robot
Data Robot is a renowned end-to-end AI and machine learning platform that facilitates quick and user-friendly implementation of reliable predictive models. Business analysts who do not have a coding background or experience can also work with Data Robot’s no-code solutions to plan, create, deploy, and upkeep enterprise-level ML and AI applications.
Some tools or platforms still require you to do some extra work and configure a portion of the desired machine-learning model. These are simply referred to as Low-code tools or platforms. Examples include PyCaret (an open-source ML library in Python), H2O AutoML (ML platform for algorithms like linear regression, deep learning, and gradient descent), etc.
Source: Datanami
Benefits of No Code Machine Learning/Low Code Machine Learning
1. Faster Deployment
No-code machine learning platforms considerably simplify deploying models from development to production. This is because they offer a simple UI for managing model deployment while accelerating the training process. They utilize robust optimization and automated feature engineering on top of cloud computing to build more accurate models faster.
2. No Coding Skills Required
For business professionals who do not have a technical background, no-code solutions are a game changer. They can quickly create machine learning models and applications with a no-code platform instead of spending hours coding and debugging. This allows for the accuracy and power of AI-based software development while saving time and money.
3. Democratization of Machine Learning
When the responsibility of creating customized business applications is extended to people beyond the IT personnel, like business analysts, marketing specialists, etc., it accelerates the business and increases the number of people working toward the solution. Since not all people have the technical advantage, no code or low code machine learning tools or services enable them to be a part of the development process, give their input, and oversee the application at each stage of development. This help in the democratization of machine learning techniques.
4. Cost-Effective
Without the automation of the development process, you will need to hire technical experts, software engineers, and data scientists. However, with no code AI or ML, non-developers can easily build and deploy models, saving a lot of time and resources while still getting the opportunity to leverage machine learning.
Limitations of No Code Machine Learning
While there are countless use cases of no code machine learning, the technology is yet to be fully developed. Some of the limitations that you might face while working with no code ML are-
1. Limited Customization Options
No code AI platforms run on pre-built templates for building projects/services. Usually, users get a drag-and-drop interface to choose and match the elements they need. While these templates come with several features, the problem comes when and if some feature is not offered by the no-code platform that you chose. The takeaway is that these platforms offer limited customization options.
2. Limited Scope of Application
No-code applications typically have limited UI and design options, making them simple and attracting more user attention. While this may be sufficient for internal procedures, no-code applications frequently need more substantial design thinking to create a user-interactive interface.
3. Lack of Transparency
Artificial intelligence and automated machine learning are primarily non-deterministic as they continually evolve, update, and revise. Given the rising applicability of these technologies, they must instill an element of trust by removing the “black box.” This is a major drawback of using no code or low code development, as most of the work is already done in the provided framework. The users or developers using the pre-built models do not holistically know how the model has been designed and whether it would be a fit for their requirements.
Now that you have an idea about no-code machine learning, you may be wondering about traditional machine learning or which is better.
Source: G2
Traditional Machine Learning
Going back to how machine learning was conceived, it started as a form of mathematical modeling of neural networks. When Walter Pitts, a logician, and Warren McCulloch, a neuroscientist, published a paper on manually mapping out human decision-making and thought processes, this is how ‘traditional’ machine learning worked. In the following sections of this blog, you will learn more about the same.
What is Traditional Machine Learning?
Source: Research Gate
As the name suggests, traditional machine learning refers to the old-school approach of coding-based algorithms that make machines learn. In this approach, programmers or developers design algorithms and logic suited to a particular problem by coding. This logic is then applied to the input data to generate the desired output. Using this logic repeatedly for each input makes it a traditional machine-learning model.
Example of Traditional Machine Learning
Using decision trees is one form of traditional machine learning. A decision tree is a flowchart-like structure that illustrates a series of choices and potential outcomes.
Consider the scenario when you wish to categorize whether or not an email is a spam. You may construct a decision tree based on different aspects of the email, such as the presence of specific phrases, its length, and the sender’s address. Each feature would be assessed at a decision tree node, and the email’s classification as spam or not would depend on the tree’s route.
Source: researchgate
Benefits of Traditional Machine Learning
While it may seem like traditional machine learning has become redundant as no code ML tools allow you to leverage machine learning without a line of code, it is not so. The traditional approach stands strong even today for a number of reasons, some of which are mentioned below-
1. High Degree of Customization
You must know that no-code machine learning tools or platforms offer built-in templates and models, making the groundwork easier in real-time. But at the same time, they leave lesser room for customization. If you want a particular feature in your ML model and the platform does not readily provide it, you will have to seek the source code and build it on top of a pre-built structure.
2. More Powerful Algorithms
Traditional machine learning applications and algorithms are often compared to modern-day deep learning and AI algorithms metrics like accuracy, speed, efficiency, etc. Regarding robustness, traditional ML algorithms are more robust as they generally require more conscious manual labor of coding and configuring. When developers know what they have done and how they have done something, it becomes easier to solve any potential problems during deployment.
3. Ability to Handle Complex Problems
While the no-code ML vs. traditional machine learning debate is always ongoing, the latter is entrusted with the ability to handle complex problems. While it is lesser complex in structure compared to deep learning models, it is easier to implement, maintain and interpret.
Limitations of Traditional Machine Learning
Despite the benefits offered by a traditional machine learning process, there are a few limitations that cause people to shift to low-code platforms. Some of these are
1. High Cost
The cost of developing a traditional machine learning model is high because you need a team of technological experts, data scientists, and ML professionals. On top of that, you will need to spare a more extended time commitment before the model is ready for development. Overall, the total cost of traditional machine learning is implicitly high.
2. Time-Consuming
Traditional machine learning is significantly more time-consuming than no-code or low-code alternatives. Significant time goes into building a model, training, visualizing, testing, and deploying it. Once the model is all set, you need to prepare the data that goes into the model, which may take a lot of time. Clearly, traditional models take more time.
3. Need for Highly Skilled Professionals
To traditionally leverage machine learning, you need a team of highly skilled professionals who can code, train, and work with tonnes of data. Besides having proficient hard skills, they must also have soft skills, like teamwork, and analytical skills, like problem-solving.
No Code ML vs. Traditional Machine Learning: Which Approach is Right for Your Business?
Source: Nitor
Now that you are familiar with both the routes to leveraging machine learning: traditional and no code, it is natural to contemplate which one is the right option for your business. But you need not worry about it. Below are some factors to consider when choosing between No Code and Traditional Machine Learning-
Factors to Consider When Choosing Between No Code and Traditional Machine Learning
1. Business Needs and Goals
Businesses and enterprises that want to create projects and applications on their own for a standard use case can opt for no code/low code machine learning as it offers pre-built generalized models. However, if your goals require customization to a greater extent, traditional machine learning would be better.
2. Available Resources
The amount of resources available also determines the approach. If you have enough resources, i.e., time, budget, experts, etc., you should be okay with a traditional approach. If you need more resources to have a team of experts, incur the costs of hiring them, and are short on time, go for no-code machine learning tools/platforms.
3. Level of Technical Expertise
Traditional machine learning requires more technical expertise and professionals, involving more extensive coding and configuration. If you have a team of experts who can work with code and pipelines, you may be better off with the traditional route. No code/low code machine learning is your best move if you lack technical expertise.
4. Time Constraint
If you are not short on time and have all the requisites, you can work well with traditional machine learning techniques. However, if you have deadlines to meet and cannot spare additional coding, configuring, or model-building time, no code/low code machine learning tools/platforms would be a safer option to ensure your delivery commitments are met.
Use Cases for No Code Machine Learning
No-code machine learning tools and platforms are widely demanded in certain use cases like customer segmentation, sentiment analysis, and image recognition. The particular reason for the same is that these applications are universally applied; hence businesses do not prefer spending additional time and resources. Read on to how no code ML helps with these use cases.
1. Customer Segmentation
No code machine learning is an excellent approach for predictive analytics, and several companies harness its potential to divide their customers into different groups (segment customers). For instance, it helps to club those customers who are likely to purchase a particular product or service based on their purchasing history or those who are likely to give feedback. This is essentially helpful for those marketers or business owners who are not into coding or developing but want to benefit from ML-driven automation.
Kellogg’s is a globally renowned consumer goods company that leverages no-code machine learning via Power BI to segment its customers.
2. Sentiment Analysis
Many applications and social networking platforms like Twitter undertake customer sentiment analysis to analyze the emotion and context behind tweets/texts. Sentiment analysis is not limited to tweets; it can also be done on support emails, feedback, or any other form of communication you have with your customers. Given the vast amount of texts or messages, opting for no code machine learning tools or platforms can accelerate the process of deriving predictive insights. This leads to quicker analysis and decision-making.
Dell, a world-famous technology company, has utilized no-code machine learning services from platforms like MonkeyLearn to save hundreds of human hours on customer sentiment analysis.
Source: altexsoft
3. Image Recognition
As no code machine learning is most suitable to answer urgent questions, the approach is widely used in image recognition. Image recognition is a process wherein ML is used to identify objects or people from a feature seen in an image or a video. Many companies working with defect detection, medical imaging, security surveillance, etc., opt for no-code or low-code ML services to utilize recognition models without the necessity of sparing time and resources for the groundwork.
Google’s AutoML is an excellent no-code solution for such use cases involving computer vision and natural language processing.
Source: azati
Use Cases for Traditional Machine Learning
This section briefs you about the common uses where traditional machine learning is still widely used despite the availability of no-code or low-code platforms. Have a look.
1. Fraud Detection
Fraud detection, as the name suggests, is all about identifying discrepancies. As per a consensus, fraud detection works most effectively using logistic regression, a supervised machine-learning technique for categorical decision-making. This is why traditional machine learning is most suitable for this use case. Almost all financial institutions benefit from ML-based fraud detection by identifying fake cheques and drafts.
2. Predictive Maintenance
Traditional machine learning is a better-suited approach for predictive maintenance as different businesses require different kinds of the latter. Types of equipment are subjective, and hence their functionality too. Consequently, you will need customized ML solutions to detect and prevent downtime. Therefore, the traditional approach is better.
Source: pci magazine
3. Natural Language Processing
While no-code ML can be helpful for some NLP tasks, more challenging NLP requirements call for using conventional machine-learning approaches. For some NLP tasks, including sentiment analysis or named entity recognition, traditional machine learning facilitates more fine-tuning and customization of models.
Furthermore, they enable a more profound understanding and control of the models, which is beneficial for retaining how the models generate predictions or locating and correcting biases.
Source: nexocode
Conclusion
So far, you have read about traditional machine learning and its modern-day alternative, no code ML. Both approaches are purposeful to leverage machine learning in your business operations and customer experience, and both have their own pros and cons. The choice between them ultimately depends on the specific requirements and capabilities of a business.
No Code tools are user-friendly and available to a broader range of users, but they might not give Traditional Machine Learning’s level of customization and optimization. While traditional machine learning demands specialized knowledge and resources, it can offer more sophisticated and individualized solutions for challenging issues.
Nevertheless, in the rapidly changing technological landscape, staying up-to-date with the latest technologies and trends is vital to continue having the edge over others. To gain more practical experience, you can head to Analytics Vidhya for courses, certifications, and other learning resources on machine learning, artificial intelligence, and data science. Analytics Vidhya is an active community of industry experts that aims to be a one-stop destination for all knowledge and career-related guidance to people seeking professional opportunities in data sciences.
Frequently Asked Questions
Q1. Is no-code better than coding?
A. No-code is an excellent approach for people with no-to-little technical knowledge as it enables them to leverage ML capabilities in their workflows. It provides pre-built models that can be readily deployed for standard use cases. But, at the same time, it leaves lesser scope for customization and creating ML pipelines for more complex workflows. So, it depends primarily on your requirements. Moreover, other factors like time, resources, and technical expertise determine which approach is better.
Q2. What is the market size of no-code machine learning?
A. The global no-code AI platform market crossed $2,581.3 M in 2021 and is expected to reach $38,518 M in the coming decade, exhibiting a CAGR of 28.1%.
Q3. Does no-code have a future?
A. With the help of no-code or low-code machine learning tools and platforms, non-technical people can also leverage machine learning without writing a single line of code. Besides, they provide ready-to-use ML models for standard use cases, significantly reducing model building and preparation time. As a result, no-code/low-code development is anticipated to take over more than 60% of the development processes that do not necessitate complex customization.
