AI is making a path for non-technical as well as technical users to have a unique, code-free, visual, and compatible platform to design and develop their models. This enables the user to have a code-free environment and boosts the prototype development.

Introduction – Say Hello to No-Code Machine Learning!

With the pacing of technological development, many changes are going on in the field of Artificial Intelligence and Machine Learning. The technical developers have a hefty stack full of many programming languages, libraries, integrated development environments, and much more. This makes the non-technical user a bit dominant when it comes to the development of various solutions.

The analysis, compilation, visualization, and development of a model or prototype requires the use of various technologies and environments that rely on good knowledge of coding. To come up with a solution that would enable non-technical users and also benefit technical users to develop a solution faster with high accuracy, AI and Machine Learning have come up with an advanced solution of No-Code Machine Learning.

Instead of writing the code from scratch, the No-Code Machine Learning platforms enable the user to drag and drop the chunks of code. This makes the solution effective, and the model is easy to analyze, build, and visualize. This process is effective and efficient. This enables the data analysts, business analysts, marketers, and other users to develop their no-code solutions as fast as possible with high accuracy. It minimizes the chances of error while coding and compilation.

Some various platforms and tools contribute to the development and upbringing of no-code machine learning. This has helped the technical programmers as well as those who say no to programming.  

Traditional Machine Learning vs No-Code Machine Learning

The traditional Machine Learning goes with the train and test models that require the user to develop the model first, train it with the sample data set, and then finally test it for the given case. The traditional Machine Learning model involves the usage of coding. The developer needs to code the model in any specific programming language. For this, the developer or analyst should have good knowledge of coding, coding platforms, along with other mathematical and statistical knowledge. 

Sometimes, this may cause compilation or run-time errors as the code is thousands of lines long. Then it involves the training of the model by using the sample datasets. The training can be via supervised or unsupervised learning. Overall, this process is time-consuming and hectic for the developer and non-technical ones can’t use this method.

Traditional Machine Learning:

• It requires coding experience
• The user should know the programming language
• The user should have good knowledge of statistics
• Should be experienced with various programming libraries and tools
• Should know error debugging

All these things are not required while using no-code machine learning platforms. All you need is the knowledge of your problem statement, the workflow of your solution, and other basic details. With all this, you are ready to drag and drop the chunks of your solution and at the end, you will get a solution model.

Importance of No-Code AI

With the growth of Artificial Intelligence, almost all sectors are using this technology to increase their growth and market. Not only the technology industry but also the business industries need a machine learning and artificial intelligence models for better ranking, research, development, and their information needs. 

The data analysts or researchers need to collect, sort, analyze, and model a huge amount of data. For this, they require to code various machine learning models. This process takes a high amount of time and is a pain for the analyst. They need to train and test many cases, debug the errors, analyze a summary report, and do many other kinds of stuff. 

To increase flexibility, reduce the development time, minimize errors, and increase the number of users, the no-code machine learning model is a great choice. This helps the data analysts to schedule their workflow accordingly and work on their research or analysis effectively and efficiently. All the work like data cleaning, processing, sorting, etc. is done within a short duration and it eliminates the chances of errors while coding. It enables the user to provide a good visualization of the solution.

Hence, it benefits the business companies and other non-technical users to have a platform where they can work on their solutions.

Benefits of using No-Code Machine Learning

No-Code platforms, brought by machine learning and artificial intelligence, reduce the barrier between the technical minds and non-technical workers like business companies so that there could be effective progress in the market.

• Business development with Artificial Intelligence and Machine Learning

There is a gap between how the business analysts approach the solution and how the developers develop the solutions. To mend this gap, so that the business analysts would understand the underlying concepts and will be able to guide the developer about the user requirements, the no-code platforms are an essential key. Only when the business analyst will understand the development process, he will be able to catch up with the developer and tell him the exact requirements. 

• Speed

By using no-code platforms the speed of development increases. It is because the data cleaning, analysis, sorting, categorization, structuring, etc. is done in a short duration. Along with speed, it is highly effective and efficient.  

• Low Cost

The automation saves lots of money that were required for developing, training, and testing the model. As we eliminate many steps so their cost gets deducted.

• No Code

Here, the user doesn’t need to know how to code. The platform welcomes non-technical as well as technical users.

• High Visualization

The solution provided by the no-code platform has high visualization which helps the user to understand the underlying concepts and mechanism with ease. This ensures that the development proceeds in the right direction and the user is not lost while developing the solution. 

• User Friendly

The platform is highly user-friendly. The user just needs to know his/her problem and solution. Based on that, he/she needs to drag and drop the segments and build the solution. 

• Minimizes Errors

As it doesn’t include coding, so we eliminate the risk of coding errors. This saves lots of time, effort, and money.

Difference between auto-ML and No-Code ML

The main difference between auto-ML and No-Code ML is that the auto-ML enables the data scientists and other developers to gain transparency and flexibility over the code. The complex pipeline of the whole machine learning model is explained thoroughly to the data scientist. Whereas, in No-Code ML, the developer or non-technical user doesn’t need to know about the specification of the code. The complex steps are not disclosed to the user. This is done to ensure that the user does not get lost while implementing the model. 

Working of No-Code ALgorithms

It includes the following steps:

• Preprocessing/Feature Engineering of data
  1. Raw data is turned into machine understandable input
  2. Data is cleaned, i.e., NULL and unnecessary data values are removed
  3. Values of numerical columns are set to accurate ranges, i.e., Normalization

• Training Models
  1. The model is trained so that it can be user friendly
  2. There are various preload settings
  3. Each algorithm has a different setting
  4. Accuracy is maintained

• Testing for Accuracy
  1. Accuracy prediction is made
  2. Accuracy is tested
  3. Accuracy report is generated

Top No-Code Machine Learning Platforms

There are abundant no-code machine learning technologies that are currently in high demand like Natural Language Processing (NLP), Computer Vision, Voice Recognition, Face Detection, and much more. 

For this, we have various No-Code Machine Learning platforms that serve the user with the best features. The most popular No-Code platforms are:

1. BigML

Link: https://bigml.com/

2. Create ML

Link: https://developer.apple.com/machine-learning/create-ml/

3. Data Robot

Link: https://www.datarobot.com/

4. Fritz AI

Link: https://www.fritz.ai/

5. Google Cloud AutoML

Link: https://cloud.google.com/automl

6. Google ML Kit

Link: https://developers.google.com/ml-kit

7. Make ML

Link: https://makeml.app/

8. Microsoft Azure Automated Machine Learning

Link: https://azure.microsoft.com/en-in/services/machine-learning/automatedml/

9. Obviously AI

Link: https://www.obviously.ai/

10. RunwayML

Link: https://runwayml.com/

11. SuperAnnotate

Link: https://www.superannotate.com/

12. Teachable Machine

Link: https://teachablemachine.withgoogle.com/

Conclusion

Overall, the No-Code Machine Learning technique has mended the gap between non-technical and technical users so that they can enjoy an effective and efficient way of developing their solutions.

For other relevant topics, refer:

1. Introduction to cuML
2. Reinforcement Learning
3. Amazon Computer Vision Services
4. Artificial Intelligence and Neuroscience
5. Kaggle Datasets to Practice NLP