Top 15 No-Code AI and ML Platforms for 2021

Artificial Intelligence & Machine Learning are the two most important technologies for businesses. They use it for building & deploying smarter models to ensure smooth operations of their business.

But the execution of such tasks requires proper coding knowledge or Data Scientists, which business organizations often lack. 

Hence, the no-code AI and ML Platforms come into the picture and help organizations to build and deploy models with no or less amount of coding.

Also, the tech giants are open sourcing their platforms to provide organizations the necessary developer tools.

This makes certain that businesses can keep up with the current development trends without the need for expert coders. 

“AI-enabled tools will generate $2.9 trillion in business value by 2021.”

                                                                                                               According to Gartner

Here’s presenting the top 15 tools to develop models in 2021 without having any expertise in coding.

No-Code AI and ML Platforms

1. Google ML Kit

Google ML Kit is available on iOS and Android operating systems.

It is a software development kit that allows the user to add the needed functions to an application with lesser codes and no or little knowledge of machine learning. 

The kit also has APIs that enable the user to make the use of custom TensorFlow Lite models on the smartphone app.

 In respect of mobile use, the kit offers a variety of features such as face detection, text recognition, landmark identification.

2. Fritz AI

Fritz AI is an amazing platform that is gaining popularity and bridges the gap between mobile developers and data scientists. 

To train and deploy the models quickly, iOS and android developers can use Fritz AI. 

Along with this, Fitz AI also provides solutions for easy deployment, analytics, and model retraining.

3. RapidMiner Studio

Rapidminer Studio allows the formation of data analytics with the use of images along with dragging and dropping features. 

The tool easily connects to warehouses, databases, and social media, enabling the user to share the access of data with anyone in need. 

As soon as the model is ready, it depicts the model quality along with explaining how it will work for the business.

4. Obviously AI

For the purpose of performing complex tasks on user-defined datasets, Obviously AI makes the use of natural language processing. 

It is basically a four-step process that starts with uploading the datasets.

After that prediction column is selected, questions are entered in natural language, and results are evaluated.

5. Data Robot

Without the need for Data Scientists or any other specialized expert, Data Robots delivers the predictive models in very little time.

To develop an accurate model with the data in hand, the tool provides access to various open-source machine learning models.

It enables the organization to enter the zone of forward-looking predictive models. 

Further, it also balances the machine learning and human experience in order to solve predictive modeling problems.

6. RunwayML

RunwayML is another great platform that lets you browse various models and provides a delightful interface. 

Without writing any code, this interface is used to train the models ranging from text and image generation to motion capture, image detection, etc.

7. What-If Tool

What-If tool can be used by anyone ranging from product managers to students as it is very simply designed. 

By creating visualizing features, the tools allow the user to make a comparison between two models simultaneously running on the same datasets. 

Any data point can be edited by the user by just adding or removing features and testing it before putting it to final use. 

The tool also allows the use of ROC curves and the confusion matrices for the purpose of determining the model precision.

8. SuperAnnotate

For the purpose of boosting your data annotation process, SuperAnnotate uses machine learning capabilities. 

It is an AI-powered annotation platform that can quickly annotate data by using video and image annotation tools.

This makes the task of datasets generation for object detection and image segmentation easier.

9. Google AI Platform

Google AI Platform is open-source, cost-effective, and easy to use.

With the support of an integrated toolchain, it helps to convert the idea of Data Scientists and engineers into reality. 

Kubeflow, Google’s open-source platform, supports AI platforms enabling the user to design portable pipelines that can be run on Google Cloud or on-premises.

10. MakeML

MakeML is such an amazing developer tool that, without coding, one can use it for building object detection and semantic segmentation models.

To create and manage datasets effectively, it provides a macOS app for iOS developers.

An interesting point to note here is that they also have datasets store to train neural networks with some computer vision datasets.

11. Microsoft Azure Automated Machine Learning 

Microsoft Azure cloud is capable of deploying ML models at a faster speed.

Using no-code UI and existing data that is filtered through algorithms and hyperparameters, Azure Automated Machine Learning is capable of automatically deploying the predictive models. 

The tool is also able to detect the errors in the data and automatically rectifies them, resulting in time savings and accurate models. 

With the help of metrics visualization, the user is able to make a comparison between two models and differences in their performances.  

12. Big ML

Big ML can be deployed as a part real-time application instantly and can be exported to any local server. 

The visual aesthetic with partial dependence allows the user to have a clear understanding of the predictive models. 

The tool has an easy to use interface and REST API, thus it provides immediate access to users.

One can access the tool on cloud or on-premises as it is available in single or multi-user version.

13. Teachable Machine

It is a web-based tool that allows a user to design such machine learning models that are easily accessible to anyone.

To make a computer learn, users can feed various examples. 

The inputs entered are then classified into different categories such as audio, image, etc.

These are then instantly checked to verify whether they are correctly classified or not.

In this way, the models can learn image classification, sound recordings, body posture and many more.

The application also provides the freedom to use files and capture live examples from the spot.

14. Create ML by Apple

Create ML is extremely simple and easy to use.

It allows the users to deploy machine learning models even without having no knowledge about it. 

Along with allowing the model building for detection, activity and sound classification, the app also makes it possible for a user to view the model creation workflows in real-time. 

15. Accelerite ShareInsights by Amazon Web Services

ShareInsights is a no-code tool that enables users to develop ETL pipelines without programming.

It also allows the use of cloud-native technologies to create interactive dashboards in much less time. 

The tool also provides machine, OLAP, and end-to-end data preparation as a single integrated process.

Summary

These are the top tools that one can use to develop models without having any knowledge of coding.

Such tools make it easy for developers to build models in less time and effort. 

However, one must ensure that he/she has the proper knowledge of these tools