In today’s fast-paced world, computers play an important role in almost every industry. A machine can carry out instructions given to it. It becomes useless if no instructions are given to it. However, with the concept of Artificial Intelligence, machines are becoming intelligent enough to work on their own.
Machine learning has a wide range of applications in today’s world. It is becoming easier with the development of new technology and languages such as Python, but it still requires a more in-depth understanding of statistics and programming. With the introduction of no-code/low-code ML, it is simple to create a model without prior knowledge of programming or statistics.
In my blog today, I’m going to describe the traditional ML approach, its challenges, how the low-code / no-code ML approach eases them, how companies can benefit, popular platforms, and more!
What is the traditional machine learning/deep learning approach and what are its challenges?
Machine learning is a technique for teaching a machine to predict future outcomes. In the traditional machine learning approach, a model must be trained using the most appropriate algorithm on the type of data. In today’s world, the data generated is not in the most appropriate structure or data to train the model, so before applying any algorithm to the data, it is necessary to arrange the data in the most appropriate manner.
The traditional approach will involve stages as shown below:
Traditional ML Approach Challenges:
- It must go through each step as shown in the above figure. By evaluating the model, if the accuracy/results of model are not sober, one must repeat the process from creating the model again or from data preprocessing process, and this will repeat until a fine accuracy is not achieved.
- During this repetitive process, a lot of time gets consumed while selecting the best model. Also, the deployment process requires time to get the program coded according to the desired User Interface of the user.
- Every stage involves a solid understanding of programming and statistics. The problem is that not all statistics specialists are programmers, and not all programmers are statisticians.
To overcome this challenge, no-code and low-code platforms were introduced.
What is the low-code / no-code ML approach and how does it help to ease challenges?
No-code ML is the process of developing a model with minimal programming and statistics knowledge. It is an AI-based algorithm that is used for coding and accuracy calculation.
As illustrated in the figure below, creating a model in the no-code ML platform becomes far too simple. It is an Artificial-Intelligence-based algorithm that chooses the best machine learning algorithm to apply to the model. A user must drag and drop the dataset and set the target variable in any no-code ML platform. The model will learn on its own. The user will then get its output, evaluation result and ready user Interface.
On the other hand, low code involves little coding work. This type of platform can also speed up the delivery of programmers, but it may necessitate some coding work. Even experienced programmers frequently use these tools to avoid creating unnecessary code which will save time and will achieve a result-oriented output.
As illustrated in the figure below, creating a model in the low-code ML platform has become so far simpler. It needs to import the dataset, structure the data, and then choose any desired low-code platform according to the type of target variable which on result, will output metric reports and validation score.
Allow me to give you a glimpse of how the traditional approach, low-code approach and no-code approach are different.
Differences between traditional, low-code, and no-code approaches:
Benefits of a No-code/Low-code ML approach:
- End-to-end implementation of a model within 3 to 4 steps along with the deployment part, just select the various ML algorithm and it will automatically select the best accuracy model for final implementation and gives the final score.
- It consumes very less time as the user doesn’t need to follow every stage of the implementation process, so it will speed up the process with result-oriented output.
- The problem of not having a solid understanding of programming and statistics is solved by this no-code/low-code platform as this platform needs less or no programming and statistical knowledge.
Now you can imagine how companies stand to gain from the use of low-code and no-code. Let’s dwell on the ‘how’ question…
How companies benefit from low code/no code
Companies will benefit from high operating speed to create applications for smooth functionality across multiple devices i.e., data collection, data manipulation, modeling, and deployment. Since it builds more apps with less time, time compatibility is no longer a barrier to innovations.
A transformation is required in today’s digital world. Low-code development simplifies the process of creating great, modern business apps. What’s more, less complexity means less turbulence.
With these low-code advantages, companies are better positioned to adapt and respond to rapidly changing business conditions.
According to NASSCOM (National Association of Software and Services Companies), companies have seen a 30-35% increase in ROI (Return on Investment) while using low code/no code platforms, up to a 75% reduction in development time, and a 65% reduction in costs.
Embrace digital evolution by cutting your development time with low-code solutions.
Now let’s turn our focus to some popular low-code and no-code AI platforms!
Top low-code and no-code AI platforms
- Pycaret
PyCaret is a Python-based machine learning application that uses a low-code approach to deploy and construct models. Professionals can use PyCaret with ease, replacing hundreds of lines of code with a few words. - H2O AutoML
This is yet another low-code platform for deploying machine learning algorithms. Linear regression, gradient descent, and deep artificial neural networks are among the features. - Auto-ViML
Professionals may use this application to quickly develop a machine learning model because it automatically renders data and finds the best results without the need for preprocessing. - Google Cloud Auto ML
This tool is a no-code solution for installing and training machine learning models for several data types. It can be used for natural language processing, video intelligence, computer vision, and translation, among other things. - Runway AI
This no-code machine learning platform can help in creating video and photo editing with no programming experience. It features filtering, green screen options, and other major features. - Lobe
Even if you do not know how to write, this no-code machine learning platform can assist you with video and photo processing. Filtering, green screen options, and other key features are also provided. - Obviously AI
Obviously, AI is for you if you want a simple way to make predictions based on data without having to write code. Marketers and business owners can use it to estimate income flow, improve corporate operations, create a more efficient supply chain, and run targeted automated marketing campaigns. All you must do is give data, select a column from which your own ML method will be built, and download your report. - DataRobot
DataRobot is a platform that lets business analysts create predictive analytics without having to know anything about machine learning or programming. To quickly build solid predictive models, the software uses automated machine learning (AutoML). For developing machine learning models, DataRobot provides a user-friendly user interface. A real-time predictive analytics solution can be deployed in only a few steps. - SkyCube
SkyCube is a no-code machine learning application that allows you to run tests on structured data without writing any code. From data exploration to feature engineering to model training and prediction, SkyCube’s graphical user interface (GUI) provides a visual tour of machine learning operations. Data privacy is extremely important to SkyCube. The SkyCube solution is totally browser-based; the entire machine learning process takes place on your computer without the need for any server communication.
I hope my blog has given you a good idea of how low code / no code platforms will ease the work dependency on programming support and will provide a desired output for non-tech experts in machine learning. ML model deployment for high load and data-intensive projects is solely substituted by this platform. But this will not replace the traditional ML process as this field has huge diversity and it will continue to update in the upcoming time.
Statistics specialists who are not experts in programming and programmers who are not statisticians are experiencing ease in their AI work due to low-code/no-code platforms. Also, a no-code/low-code ML platform provides result-oriented output within a very less stipulated time.
Send us an email to share your views about this blog and visit us at Nitor Infotech to learn about how we use leading-edge technology to fortify the IT ecosystems of companies. On a related note, you can also check out this blog if you’d like to know about Machine Learning as a Service.
Our recent webinar on ‘Low-Code and Beyond: Exploring the Future of Application Development and Your Career’ had our technology experts talk about how Low Code plays a compelling role in deploying responsive web applications and its future as a career path.
This webinar highlighted how:
- Why low code is the future of application development
- How your career can shift gears as a low code developer
- What will change in the app development process with low code
- Where a low-code platform like Outsystems can turn things around
Happy learning!
