‘No-Code’ Machine Learning Platforms

Machine learning and artificial intelligence are the two fields of technology that are rapidly advancing and are being used in different sectors like finance, education, business, and many more. ML and AI based models are deployed in regular activities, making the process smoother and easier for everyone.

 

(Recommended read: Top 6 Machine Learning Techniques)

 

But, as convenient as artificial intelligence is, it is only used in 25% of organizations, according to nanalyze. This is because all AI and ML models require coding! It takes a lot of investment to build any custom AI model or hire any data scientist for the work. Many businesses lack the proper knowledge and hence, end up using more traditional technologies to do the work. 

 

As a solution to this problem, new ‘No-Code’ platforms have been introduced that help organizations execute AI-based work without prior coding knowledge or with the help of minimal coding. 

 

In this article, we are going to list out the top Machine learning platforms that help in developing models without any coding experience.

 

List of ‘No-Code’ ML platforms

 

1. BigML:

 

It is an open-source platform that provides machine learning services for business analysts and application integration.

 

BigML was created in 2011 with the goal of developing a machine learning platform that assists businesses in sorting through data libraries and making data-driven choices across all sectors and it can build Machine learning and deep learning models without any requirements of codes. 

 

It provides a unique web interface for uploading datasets, creating descriptive predictive models, and evaluating machine learning models. More flexibility is provided via Command Line Interface (known as bigmler). There is also a REST API that may be used in any programming language, including Python, Ruby, and Java, as a wrapper. 

 

(Also read: 10 Machine Learning Project Ideas for Beginners)

 

 

2. MonkeyLearn:

 

It's another no-code machine learning application that analyses language from internal CRM systems, social media, emails, documents, online reviews, and other sources.

 

The solutions from MonkeyLearn enable real-time analysis for instantly usable information and data-driven choices. It is entirely extensible, and one may use pre-trained models right immediately or train their own to their unique needs and criteria in a matter of minutes. 

 

There are inbuilt sentiment analyzers, keyword extractors, feedback, and email intent classifiers that help the users in performing many functions. 

 

MonkeyLearn's no-code approach saves a lot of time and money by streamlining operations, boosting marketing and market segmentation, and monitoring what customers are saying about the brand all over the internet.

 

(Related read: Sentiment Analysis of YouTube Comments)

 

 

3. Create ML:

 

This no-code ML tool was created by Apple and can be executed only by Mac users for training custom machine learning models. Users may train models to accomplish tasks such as picture recognition, text extraction, and discovering connections between numerical quantities. 

 

According to analyticsindiamag, these are some of the features of Create ML:

 

• It can build powerful on-device models with user-friendly interfaces and can train multiple models using several datasets in a single project.

• It integrates an external graphics processing unit with the Mac for improved model training performance, and the model performance can be seen on the Mac utilizing continuity with the iPhone camera and microphone. 

• One can learn and create at their own pace and can pause, save, resume, and extend the training process accordingly.

 

4. Google AutoML:

 

Google AutoML enables users to leverage the power of artificial neural networks to create successful prediction models from normal text and picture data, and interfaces with Google Sheets, Google Slides, and other services make it simple to get started. 

 

AutoML operates entirely in the cloud, so no infrastructure is required to get started. Image categorization, NLP analysis, AutoML translation, and video intelligence are all available through Google's advanced analytics. 

 

Because of Google's decades of expertise with ML models, their pre-trained models are frequently fully usable right out of the box, and their UX makes training bespoke models reasonably painless for the AI novice.

 

 

5. Obviously AI:

 

Obviously AI is one of the best no-code machine learning tools for making data-based predictions. It promises to take users from data collection to machine learning analysis in just a few clicks - upload a CSV file, select the needed data analysis, and immediately see the findings, including replies to direct queries using Natural Language Processing and natural language understanding. 

 

Obviously AI identifies the best method for every use case, so training is expedited and models may be deployed immediately, albeit the amount of customization may be slightly lower than on other platforms. 

 

Their "what-if" scenarios, on the other hand, can provide actionable insights within a few minutes of starting — excellent for the non-programmer. Marketers and company owners may use it to anticipate income flow, manage business operations, create a more efficient supply chain, and run tailored automated marketing campaigns.

 

 

6. Fritz AI:

 

Fritz AI is primarily intended to assist smartphone app designers (iOS and Android) in quickly and easily integrating machine learning technologies without requiring strong expertise in data science. As they are utilized in software development, many of the connectors do need some coding. 

 

Certain e-commerce and augmented reality solutions, on the other hand, may be built using no-code technologies. The model development Studio may be used to train customized services or to get straight in with pre-trained models and projects. Fritz AI's two main products are Fritz AI for mobile and Fritz AI for SnapML.

 

(Related reading: Machine learning algorithms)

 

 

7. Google ML Kit:

 

Another mobile software development kit that requires no code to develop Android and iOS applications is the Google ML kit. With this kit, the functionality may be implemented in only a few lines of code, without the requirement for machine learning skills. 

 

ML Kit may be used offline to process pictures and text that must remain on the device. It makes use of the machine learning algorithms that underpin Google's own mobile experiences. It also combines machine learning models with complex processing pipelines and makes them available through simple APIs to allow strong use cases in apps.

 

 

8. RunwayML:

 

RunwayML is a no-code platform that makes machine learning (ML) approaches available to students and creative producers across several fields. It provides user-friendly website interfaces that make the process fun and easy. 

 

RunwayML also links to a number of creative programming and design environments and works as a plugin with software programs. One may also train their own models to create pictures and recognize things in photos.

 

 

9. Teachable Machine:

 

Teachable Machine is a no-code tool for building machine learning models for your websites, applications, and other projects. The web-based application is offered by Google and can teach a computer to recognize pictures, sounds, and poses. 

 

It offers a friendly interface for users who do not have any prior knowledge of machine learning or coding. Teachable Machine may be the most user-friendly no-code machine learning tool when it comes to image identification.

 

 

10.  Make ML:

 

MakeML is a no-code tool for building neural networks for object identification and segmentation. The technology is designed to make the training process simple to implement. It is intended to manage data sets, training settings, markup, and model training procedures. 

 

Fast training on the cloud on GPU instances, as well as a convenient markup tool for generating datasets, are among the benefits. There is no requirement for Python code.

 

 

11. DataRobot:

 

DataRobot is a well-known end-to-end corporate AI platform that allows for the quick and easy deployment of reliable prediction models. All of the procedures required to design, construct, deploy, monitor, and manage strong AI systems at a corporate scale are supported by DataRobot automated machine learning software. 

 

 

12. Microsoft Azure Automated Machine Learning:

 

Automated ML in Azure Machine Learning is a no-code solution for training and tuning a machine learning model. It decentralizes the model creation process and enables users to define an end-to-end machine learning pipeline for any problem.

 

It may be used to build ML solutions without substantial programming skills, save time and money, use data science best practices, and provide rapid problem-solving.

 

(Related blog: What is Microsoft Azure and Why it is Used?)
 

 

13.  Nanonets AI:

 

Nanonets provide intelligent document processing. It automatically extracts data from documents, saving hours of the laborious document management of the employees or users. 

 

Nanonets AI analyses unstructured, semi-structured documents, even if they do not adhere to a predefined pattern, automatically verify data, and improves over time through repeated usage.

 

 

14. Rapid Miner:

 

RapidMiner is a data mining application. It is founded on the premise that business analysts and data analysts do not always need to program in order to accomplish their jobs. 

 

At the same time, mining necessitates data, therefore the tool was outfitted with a good set of operators capable of performing a wide variety of activities for acquiring and processing data from diverse sources like files or databases. Overall, this technology simplifies data analytics to the point that anyone can use it.

 

 

15. Super Annotate:

 

SuperAnnotate is a complete platform for annotating, training and automating computer vision pipelines. This solution allows you to scale annotation and computer vision projects of any size by utilizing the smartest technologies, strong data management systems, and outsourced services.

 

(Also read: Top 15 Machine Learning Hackathons)

 

Conclusion

 

Although No-code ML platforms are gaining popularity rapidly, they cannot replace the quality of data-intensive custom-built ML models that are built by actual data scientists and engineers via coding and practice. 

 

Although, using the right tool that caters to the specific requirements can help immensely if one wants to build a model in such platforms. To make things easier, we have listed out the top 15 no-code Machine learning platforms and how they work.