No-Code Machine Learning Platforms in 2024

Introduction

No-code machine learning is the new electricity, in the same way that data is the new oil. Machine learning is being used everywhere. No-code machine learning revolutionizes every industry and changes every aspect of our world. The state of predictive analytics and machine learning today is not yet ideal. In this article, we outline the state of no-code machine learning platforms and tools. This article documents our process, the metrics we used, and our insights.

Overview of machine learning and its role in AI development

Machine learning is currently reserved for expert data science teams and large enterprises. 40% of companies claim that AI technologies and expertise are too expensive (Deloitte).There are far more data-related business problems than data scientists. 83% of businesses say that AI is their strategic priority today. There are, however, not enough data scientists. There is a considerable gap between AI and business domain experts. They don’t speak the same language and rarely understand each other.

Our definition of no-code machine learning platforms

The no-code movement is here, and heading towards maturity. In particular, no-code machine learning. Machine Learning isn’t about typing code, it’s about business value. No-code machine learning platforms enable domain business experts to test their ideas. No-code machine learning platforms are tools that do not require any coding. No-code tools should not require users to have in-depth knowledge about machine learning, AI, programming, or software development. No-code machine learning platforms fill gaps in businesses that don’t have in-house AI talent. No-code machine learning is a suitable option for non-technical people. No-code machine learning platforms enable everyday business users to create fantastic machine learning applications. Application development through no-code machine learning unlocks many value opportunities for businesses.

The benefits of no-code machine learning platforms

• Simplifying complex AI-based tasks: Instead of spending hours coding and debugging, business users can build machine learning models and applications with a no-code platform. This saves time and resources and reaps the benefits of using AI-based data analysis. Business users can create AI models, and deploy their AI applications into production. 
• Increased efficiency: No-code machine learning platforms help to increase the efficiency of predictive analytics projects. No-code solutions automate processes. This helps you reduce the amount of time needed to create a successful model. 
• Easier model deployment: No-code machine learning platforms simplify deploying a machine learning model from development into production. No-code solutions provide an easy-to-use user interface for controlling the deployment of models. 
• Faster model training: No-code machine learning platforms can speed up the training process. No-code machine learning platforms use powerful optimization algorithms and automated feature engineering. Models can be trained significantly faster and at a much lower cost. In turn, this leads to faster experimentation and building better predictive models.
• Cost savings: No-code machine learning platforms offer significant cost savings, compared to traditional software development. No-code solutions remove the platform-related costs, such as hardware and labor. No-code solutions are often much less expensive to buy and maintain. No-code solutions help to reduce operational costs in the long run.

Our research on no-code machine learning platforms 

In our research on no-code machine learning platforms, we chose three primary metrics:

• Platform simplicity: How simple is it for a user with minimal to no coding experience to use the platform? Is machine learning accessible to non-tech users through the self-explanatory user interface? What about results after ML model training? Does the platform present them in an easy-to-interpret way? The easier the no-code machine learning platform is to use, the better score it will get (1-10). 
• Number of use cases covered: No-code machine learning platforms tend to have positioned themselves either in different technologies. These include Predictive Analytics, Natural Language Processing, or Computer Vision. Specific business use cases (classification problems, CRM, web-builders, business apps). Some no-code machine learning platforms are oriented toward the process. These no-code machine learning platforms help end users manage the machine learning process. Other no-code machine learning platforms are oriented toward use cases. They enable end users to quickly train and deploy a machine learning model for a specific use case. Is the platform more focused on a specific use case, or is it more general? If the no-code machine learning platform can cover a higher number of use cases, the higher score it will get (1-10).
• Target customers: We place a particular emphasis on the platform’s target customer profile. We divided no-code machine learning platforms into two categories. One, suitable for SME, and another, suitable for enterprise. We based our segmentation on targeted users, billing, required technical setup, and the minimum knowledge needed. It’s important that the realm of using ML models is not restricted to technical users and technical people.

We went through each platform’s landing page, documentation, features, and tutorials. We created a score on the metrics we had decided upon for each no-code machine learning platform. Please note that this list of no-code machine learning platforms is incomplete. We could also include some other metrics for comparison. We are particularly interested in mapping out no-code machine learning platforms that are suitable for SMEs, easy to use, and have many use cases.

No-code machine learning platforms

CreateML  

CreateML is a no-code platform by Apple that creates and trains custom machine learning models. CreateML is an independent macOS application that includes a range of pre-trained model templates. CreateML can process images, videos, photos, tabular data, and texts as inputs. From that input, it will build classifiers and recommender systems. With CreateML,  you need to parse the training and validation data in the required formats, which can get quite technical. It’s also possible to fine-tune the metrics and set your own iteration count before starting the training.

DataRobot

DataRobot was founded in 2012. DataRobot aims to democratize data science and automate enterprise end-to-end machine learning processes. DataRobot enables data scientists to build predictive analytics without machine learning programming. DataRobot is based on open-source algorithms and automated machine learning (AutoML).

Google Cloud AutoML

Google Cloud AutoML works in the cloud. Google Cloud AutoML includes Vision for image classification, Natural Language Processing, AutoML Translation, Video Intelligence, and Tables. With Google Cloud AutoML, developers with only limited machine learning expertise can train use case-specific models. Google Cloud AutoML works with different types of data. Google Cloud AutoML also covers a broad range of use cases. It can be difficult to operationalize results with Google Cloud AutoML if you’re not a developer. 

Graphite Note

The complexity of machine learning poses a significant challenge, especially for non-technical professionals. This is where Graphite Note comes in. Graphite Note is a no-code machine learning platform with a focus on “business value first.” Graphite Note simplifies the use of machine learning in analytics. Graphic Note enables business users to generate machine learning models without needing to code. Graphite Note empowers decision-makers, strategists, and execution teams, to understand key business drivers and predict potential scenarios using their business data.

One of the unique features of Graphite Note is its built-in data storytelling capabilities. Graphite Note transforms numbers, graphs, and charts into meaningful presentations. Graphite Note makes complex data insights understandable for everyone in the team. This feature is particularly beneficial for business analysts, data scientists, and project managers. Graphite Note aims to be the world’s easiest-to-use no-code machine learning platform. Graphite Note gives you a single platform to build, visualize, and explain Machine Learning models.

Levity

If image, text, and document classification is the thing you need, Levity may be just the platform you need. Levity enables end users to train custom models on their use-case-specific data. Levity is suitable for SMEs and Enterprises. Levity models automatically learn from user interactions, as they ask for input when unsure. Levity provides an end-to-end solution that integrates with daily tools business people use.

Lobe  

Lobe is a Microsoft product. Lobe offers advanced image classification, with object detection and data classification. Lobe simplifies the process of machine learning into three easy steps. First, you collect and label your images. Then, you train the model and review your results. After that, you can play, improve, and export your model. Lobe is a free desktop app with a reasonable amount of pre-trained solutions. When you have completed the model training, you can export your model to industry standard formats. You can then ship it on any platform you choose.

MakeML

If you need to detect objects and segment them without manual coding, you should check out MakeML. MakeML can help you to solve a business problem using Computer Vision in a couple of hours. MakeML provides a way to create and manage datasets.  MakeML has shown its potential in sports-based applications and ball tracking. MakeML has end to end tutorials for training segmentation models. This gives non-machine learning developers a good head start.

MonkeyLearn  

MonkeyLearn uses unstructured text-based data to get content topics, sentiment, intent, or keywords. It makes it easy to clean, visualize, and label customer feedback. MonkeyLearn is also an all-in-one data visualization and no-code text analysis studio. MonkeyLearn lets you use the ready-made machine learning models. MonkeyLearn also enables you to build your own. For a start, you may choose from a wide range of pre-trained classifiers. MonkeyLearn is a great tool to simplify text classification and extraction processes.

Noogata

Noogata is another no-code machine learning platform focused on eCommerce companies. With Noogata, you can automate omnichannel retail analytics and reporting. You can also integrate data from eCommerce marketplaces, advertising platforms, and direct-to-consumer platforms. These all go into a single cloud-based data warehouse. Noogata uses dozens of pre-built, ready-to-go ML models to turn data into insight.

Obviously.ai

You can predict tabular data within minutes with Obviously.ai. Obviously.ai enables everyone to start making predictions. Using the low-code API allows dynamic Machine Learning predictions to feed into your application. Obviously.ai supports time series forecasts, classification, and regression problems. Obviously. ai is handy for SMEs looking for a tool that automates the entire ML process. 

Pecan

Pecan AI is a predictive analytics tool. Pecan AI enables you to gain foresight into the metrics that matter most to your team. Pecan’s use cases cover demand forecasting, churn prediction, and conversion modeling. Pecan’s predictive analytics insights inform customer acquisition and retention tactics, pricing and packaging, resource planning, production and distribution.

RapidMiner

RapidMiner is designed for data mining. Its main idea is that business analysts don’t have to program code to do their job. RapidMiner has a good set of operators solving various tasks for obtaining and processing information from multiple sources. RapidMiner makes data analytics simple enough for any business user to use it.

RunwayML

RunwayML is a great no-code machine learning platform for creators and makers. RunwayML allows machine learning techniques to be accessible to students and creative practitioners. Its excellent visual interface makes it easy to train your models. RunwayML supports text, image generation, and motion capture.

SuperAnnotate  

SuperAnnotate helps you automate your AI pipeline faster. SuperAnnotate is an end-to-end platform to annotate video, text, and images with data throughput. You can also build high-quality datasets using services and toolsets. SuperAnnotate offers active learning and automation features. This helps you speed up your annotation process.

The future of no-code machine learning platforms

70% of new applications will use low-code or no-code technologies by 2025. That is up from less than 25% in 2020 (Gartner). As we look ahead, the no-code machine learning platforms landscape will continue to evolve and improve. This will make machine learning more accessible to a broader range of users. We are very enthusiastic about the opportunities that no-code machine learning platforms bring. It has become a very dynamic space, and we can’t wait for further progress. We are particularly interested in bringing the power of machine learning to SMEs. We must remain aware that no-code machine learning will not and can not replace data scientists and technical users in all cases. No-code machine learning platforms won’t altogether remove the need to write code. No-code machine learning platforms empower business domain experts to test their ideas to solve business problems. This speeds up the process from concept to production.