But, the state of predictive analytics and machine learning today is not ideal:
Today, machine learning is 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 who can solve them. 83% of businesses say that AI is their strategic priority today, and yet, there is not enough data science talent. (Forbes)
There is a considerable gap between AI and business domain experts - they don't speak the same language and rarely understand each other.
Early on, we discovered how difficult it was for non-technical people to build AI solutions or even to understand what benefits AI can bring.
No-code movement is here, becoming more and more mature. There are specific aspects of no-code that are arising nowadays - and that is no-code machine learning.
We imagine a world where an easy tool will eliminate machine learning programming for well-defined business problems.
Because Machine Learning isn't about typing code, it's about business value.
And that is the reason why no-code machine learning platforms so inspire us!
They create opportunities for domain business experts to play around and test their ideas without getting lost in translation, without AI/ML experts, and without even knowing they are "doing AI or ML."
We are building the world's easiest-to-use no-code machine learning platform - Graphite Note. That means we are following this space all the time. If you are interested in no-code and no-code machine learning platforms, we believe that sharing these insights would also be helpful for you.
In this article, we are evaluating the state of no-code machine learning platforms and tools. This article documents our process, the metrics we used, and insights we could draw from our assessment.
What is no-code machine learning anyway?
First, we need to define which platforms we are going to consider.
No-code machine learning platforms should be tools that do not require any coding and ideally don't require any knowledge of machine learning, AI, programming, or software development concepts.
No-code machine learning platforms help fill the gaps in the business that don't have in-house AI talent. No-code machine learning is a suitable option for non-technical people because it is less intimidating.
This new technology allows everyday business users to create fantastic Machine Learning applications without writing a single line of code.
70% of new applications will use low-code or no-code technologies by 2025. That is up from less than 25% in 2020. (Gartner)
No-Code Machine Learning Platforms: Our Research
Our next step was to agree on the assessment metrics. In order to simplify things, we chose three primary metrics:
Platform simplicity and ease of use
Number of use cases covered
Then we went through each platform's landing page, documentation, features, and tutorials. We created a score on the two metrics we had decided upon for each no-code machine learning platform.
Metric: Platform simplicity and ease of use
This metric is, in our opinion, a bread and butter or no-code platform.
How simple can a user with minimal to no coding experience 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?
Based on all these answers, the easier the no-code machine learning platform is to use, the better score it will get (1-10).
Metric: The number of use cases covered.
Platforms in the no-code machine learning space tend to have positioned themselves either in
Different technologies like 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: they help end users manage the machine learning pipeline and process.
The others are oriented toward use cases; they enable end users quickly train and deploy a machine learning model for a specific use case.
We are considering how much of the whole machine learning process does the platform cover? 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).
Metric: Target customer
We put particular emphasis on the platform's target customer profile. Ultimately, we divided no-code machine learning platforms into two categories:
Suitable for SME
Suitable for Enterprise
Our segmentation is based on targeted users, billing, the amount of technical setup required, and the minimum knowledge needed to start working with a platform.
Please note that this list of no-code machine learning platforms is incomplete. We could also include some other metrics for comparison. Still, we are particularly interested in mapping out no-code machine learning platforms suitable for SMEs and easy to use while having many use cases available. We will happily add new no-code machine learning platforms as they come.
Let's take a brief look now into selected no-code machine learning platforms.
No-Code Machine Learning platforms
If you are into iOS development, you probably started with Apple's no-code drag and drop platform, CreateML.
This is a no-code platform by Apple to create and train custom machine learning models on Mac. It is an independent macOS application that comes with a bunch of pre-trained model templates.
The platfrom can take images, videos, photos, tabular data, and texts as an input. From that input it will build classifiers and recommender systems.
You need to pass the training and validation data in the required formats, which can be too technical for most people at the moment.
You can also fine-tune the metrics and set your own iteration count before starting the training.
DataRobot was founded in 2012. Its mission was to democratize data science and automate the enterprise's end-to-end machine learning process. The platform enables data scientists to build predictive analytics without machine learning programming. It is based on open-source algorithms and automated machine learning (AutoML) to find the best model and generate accurate predictive models.
Google's response to Apple's Create ML is - Google's Cloud AutoML. The principle is the same, but AutoML works on the cloud.
The platform includes Vision for image classification, Natural Language Processing, AutoML Translation, Video Intelligence, and Tables in its machine learning products.
Developers with only limited machine learning expertise can train models specific to their use cases.
So, this platform works with different types of data. It also covers a broad range of use cases: from video intelligence and computer vision to NLP and translation.
But, it's hard to operationalize results if you're not a developer.
Graphite Note simplifies the use of Machine Learning in analytics by helping business users to generate machine learning models without coding.
The platform's focus is on "business value first." Graphite Note aims to be the world's easiest-to-use no-code machine learning platform.
It provides a single platform to build, visualize, and explain Machine Learning models for real-world business problems and use cases.
With business data as input, decision-makers, strategists, and execution teams can understand key business drivers and take this understanding to the next level by predicting potential scenarios without writing a single line of code.
It is the only predictive analytics tool on the market with built-in data storytelling features. With a data story, business users can transform numbers, graphs, & charts into meaningful presentations that everyone in their team can understand.
If image, text, and document classification is the thing you need, Levity may be just the platform you need. It enables end users to train custom models on their use-case-specific data. They are suitable for both SMEs and Enterprises. Levity models will automatically learn from user interactions since they ask for input when unsure. This tool focuses on providing an end-to-end solution that integrates with all the daily tools business people use.
Lobeis a Microsoft product. It 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.
It is a free desktop app with a reasonable amount of pre-trained solutions. When you are done with the model training, you can export your model to various industry standard formats and ship it on any platform you choose.
If you need to detect objects and segment them without manual coding, you should check out MakeML. This no-code machine learning platform can help you to solve a business problem using Computer Vision in a couple of hours.
It provides a way to create and manage datasets, such as performing object annotations in images.
MakeML has shown its potential in sports-based applications and ball tracking. They have an end to end tutorials for training segmentation models, which should give any non-machine learning developer a good headstart.
MonkeyLearn is another no-code platform that uses unstructured text-based data to get content topics, sentiment, intent, or keywords. It makes it easy to clean, visualize, and label customer feedback.
It is also an all-in-one data visualization and no-code text analysis studio, so you can have insights into your data and analyze it.
This platform lets you use the ready-made machine learning models while allowing you to build your own. For a start, you may choose from a wide range of pre-trained classifiers.
It is a great tool to simplify text classification and extraction processes.
Noogata is another no-code machine learning platform focused on eCommerce companies.
Here you can automate omnichannel retail analytics and reporting without programming any code. You can also integrate data from eCommerce marketplaces, advertising platforms, and direct-to-consumer platforms into a single cloud-based data warehouse.
Noogata uses dozens of pre-built, ready-to-go ML models to turn data into insight.
You can predict tabular data within minutes with the Obviously.ai no-code machine learning platform.
It enables everyone to start making predictions. Using the low-code API allows dynamic Machine Learning predictions directly into your application.
They support timeseries forecasts, classification, and regression problems. The platform is handy for SMEs looking for a tool that automates the whole ML process. With some other potential use cases and usability improvements, Obviously.ai will be even more powerful.
Pecan AI is a predictive analytics tool that allows you to gain foresight into the metrics that matter most to your team. Their use cases cover demand forecasting, churn prediction, and conversion modeling. The platform's predictive analytics insights inform customer acquisition and retention tactics, pricing and packaging, resource planning, and production and distribution.
RapidMiner is a platform specifically designed for data mining. Its main idea is that business analysts don't have to program code to do their job. The tool is prepared with a good set of operators solving various tasks for obtaining and processing information from multiple sources, like databases and files. Across the board, this tool makes data analytics simple enough for any business user to use it.
RunwayML is a great no-code machine learning platform for creators and makers. It allows machine learning techniques to be accessible to students and creative practitioners from various disciplines. Its excellent visual interface makes it easy to train your models quickly. RunwayML supports text, image generation, and motion capture.
SuperAnnotate is one of the no-code machine learning platforms that helps you automate your AI pipeline faster by using a robust toolset and industry-leading annotation services.
It 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. It offers active learning and automation features that help you make your annotation process faster.
Power your business with machine learning, without writing code.
We are very enthusiastic about the opportunities that no-code machine learning platforms bring to the table. 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 via real-world use cases they need and understand.
Nevertheless, we all must be aware that no-code machine learning will not and can not replace data scientists. It won't altogether remove the need to write code.
But, it will empower business domain experts to play around and test their hypotheses and ideas for well-defined business problems, speeding up the process from concept to production.
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