Enhancing the proficiency of machine learning can be tough. Especially if it comes to business applications (AI influenced the rise of the economy). Using computers for stimulating the human intelligence process seems to be more complex and demands many algorithms (data processing + analyzing, training machines, creating models, predicting). The process takes much effort and demands high-rated programming/coding skills.
Up till today, humanity has tried to create ways of enhancing the proficiency of machine learning. People aimed at making the definitions “useful” and “ easy” equal.
Have you wondered about something as unique and easy as low-code machine learning? Have you known that there are no-code platforms?
They serve as the easiest way to build applications (in business, financial field, medicine, etc.) without any extra skills and specific knowledge. These are the accessible machine learning tools. With these tools, the companies can make their projects quicker (either big or small). Small companies are enabled to try machine learning platforms. At the same time, bigger companies can no longer be bothered with data science.
- The advantages of low-code and no-code platforms:
- Easily trained models of machine learning
- Quicker predictions (AI) –without too much coding
- Can be used by non-programmers
- Reduced IT costs
- How do these platforms work? No-code vs Low-code platforms.
Contents
No-code platforms:
No-code and low-code platforms have much in common. Even though, no-code platforms are characterized by their specific features and approach to issue solving.
No-code platforms were created to substitute the high-leveled programmer’s work. They are necessary to fit the users with a lack of knowledge in the sphere of programming language. With the appearance of such a tool, it becomes easier to create a model because all you need to do is to load the needed data and let this platform select the needed information for creation by pushing the buttons.
Generally, no-code platforms were designed for business users mostly. They are based on the existing patterns and able to automatically accomplish machine learning algorithms (we can load the data and choose the necessary model instead of training this model, predicting and improving it by yourself).
No coding apps require no programming at all. That makes its’ users more framed in creating their apps because there is an existing base of patterns (+programming interface) that enable the platform to do most of the work on the app creation (there are special templates with an example of ready-made questionnaires ( like Quora)/ design of profiles, etc.)
My favorite compilation of no-code and low-code applications are mentioned here.
Imagine that you are a child. And your data for application creation is like a shaped figure (let it be a square, for instance). There is also a box with many holes of different shape. The child’s task is to put this square in the necessary one. If this child is unskilled in this game-he won’t do it by himself. He will need someone to give him a hint and help find a necessary hole.
So, everything is simple: shapes, boxes, shaped holes for the items with the same shape. This child is the user; the box is the interface; the square is the data that the user possesses; the helper is the no-code platform that he/she uses at the moment.
All you need to achieve your goal is some trivial actions performed under the guidance of your helper. As a result, we get a fast and simple product, just as simple as this game.
- What are these platforms? Google Machine Learning Kit
-Created for iOS and Android developers;
-Identification functions: face scan; language identification; object detection, etc.
-Real-time use on your phone (fast processing)
- Google Cloud Auto Machine Learning
-multiple kinds of data accepted
-performs faster
-advanced level of predicting by picking the most suitable features for the given problem
- Teachable Machine
-apps+websites;
-convenient and simple interface;
-recognition of photos and images
-sounds recognition either
- Runway Artificial Intelligence
-for non-programming people (users)
-for editing (photos, screen, filter included)
-creative tool
- Lobe
– suitable for the first project (ML);
-fresh platform (that’s why not many functions are available);
– images and their classification;
– quite typical for advertisers + business users;
- Obviously Artificial Intelligence
-available for marketing and business projects
-good for optimization of business procedures, etc.
There are many other platforms: Create Machine Learning, Fritz Artificial Intelligence, and others.
Analyzing this list, we can see that the platforms respond to the leading goal of no-code machine learning, which is to adapt machine learning to business and marketing users. Thus, in adapting the interface for convenience of projecting and avoiding complex unknown algorithms.
Low-code platforms:
Firstly, we should state that the servers of the low-code platforms are developers, while no-code machine learning is oriented on business users. Unlike no-code platforms that cease the complexity of algorithms for their’ users, low-code platforms help employees grasp the role of the data in their projects ( they are enabled with coding and error correction in difference from no-code users). Moreover, low-code machine learning suggests that we count less on data scientists (in making decisions).
Therefore, if it comes to the decisions made by the employees, then the data influence becomes more explicit with the help of a low-code platform.
Also, unlike no-code platforms, for low-code better customization is available. Despite this, low-code machine learning users can create more complex applications.
What are these platforms?
- PyCaret
-transforms an abundance of code lines into shorter phrases/words;
-available for the beginning level;
- Auto-ViML
-greatest automatization of data processing;
-quicker modeling
- H2O AutoML
-multiple modeling is faster without specific experience
Disadvantages and summary:
Looking through the possibilities and features of both low-code and no-code platforms, we can see that their appearance makes machine learning much easier for the less qualified programmers and other users. But still, we can’t state that this approach is ideal.
Here is why:
1) (Low-code ): requires advanced knowledge when it comes to the better customization
2) (No-code): minimal customization
3) (Both low-code and no-code have):
-too much the dependence on the tool;
-extra algorithms and actions are restricted;
-dependence on the data;
-limitations in the problem-solving scale
