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)
Benefits of No-Code Machine Learning Platforms
1. Simplifying complex AI-based tasks
Simplifying complex AI-based tasks is one of the main benefits of no-code machine learning platforms. Instead of spending hours coding and debugging, business users can quickly and easily build machine learning models and applications with a no-code platform. This saves time and resources while still gaining the accuracy and power of using AI-based data analysis. Not only can business users quickly create AI models, but they can also easily deploy their AI applications into production. All of this is possible without understanding complex coding or software development skills.
2. Increased Efficiency
No-code machine learning platforms can help to increase the efficiency of predictive analytics projects. By automating processes, such as data preparation and model selection, no-code solutions can help organizations reduce the amount of time needed to create a successful model.
3. Easier Model Deployment
No-code machine learning platforms make deploying models from development to production significantly easier. This is because many no-code solutions provide an easy-to-use UI for controlling the deployment of models.
4. Faster Model Training
No-code machine learning platforms can speed up the training process by using powerful optimization algorithms and automated feature engineering. With the addition of cloud computing and AI-assisted optimization, models can be trained significantly faster and at a much lower cost. This makes it faster to experiment with different hypotheses and build a better predictive model.
5. Cost Savings
No-code machine learning platforms can provide significant savings when compared to traditional software development solutions. This is because many platform-related costs, such as hardware and labor, are removed from the equation. Additionally, no-code solutions are often much less expensive to purchase and maintain than traditional ones and can help reduce operational costs in the long run.
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.
In today's data-driven world, businesses across various industries - from healthcare to finance, retail to manufacturing - are constantly seeking ways to leverage their data for strategic decision-making. However, the complexity of machine learning often 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 designed with a focus on "business value first." It simplifies the use of machine learning in analytics, enabling business users to generate machine learning models without the need for coding skills. This 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. It transforms numbers, graphs, and charts into meaningful presentations, making complex data insights understandable for everyone in the team. This feature is particularly beneficial for professionals in roles such as business analysts, data scientists, and project managers, who often need to communicate data insights to various stakeholders.
By addressing the pain points of complexity and accessibility in machine learning, 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, making predictive analytics accessible and understandable for all.
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.
As we look ahead, the no-code machine learning platforms landscape will continue to evolve and improve, making ML more accessible to a broader range of users. The platforms listed here, including our very own Graphite Note, are leading the charge, but always keep an eye out for new entrants and advancements in this exciting field.
Trust in the Right Tools
Choosing the right no-code machine learning platform is a significant decision, and it's important to trust the tools and resources that you use. That's why at Graphite Note, we're dedicated to maintaining transparency and open communication with our users. We regularly update our platform based on user feedback and industry advancements to ensure you have the most powerful and user-friendly tools at your disposal.
We invite you to explore the capabilities of Graphite Note and see how our platform stands among the top no-code machine learning solutions. We're here to answer your questions and help you harness the power of machine learning for your business.
Note: The post content is reviewed and updated periodically to ensure its relevance and accuracy. Last updated: [2023-05-13]
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 in all cases. 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.
This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies. Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.
At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.
Hrvoje Smolic, born in 1976 in Zagreb, Croatia, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.
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