A no-code development platform is a visual development interface that allows developers to create mobile and web applications using established templates, pre-built logic models, drag-and-drop application components, links to other components, and so on, all without knowing how to code.
- No-code technology is mainly meant for business users, enabling them to easily transform corporate use cases into applications on their own. Surprisingly, this “Zero-Code” platform does not demand users to have prior coding skills in creating applications using no-code.
- No-code software development decouples programming languages and syntax. This ax from logic in favor of a visual approach to software development allows for faster delivery.
- Many AI and machine learning companies claim to democratize AI, which is true for their target users, who are still primarily engineers. Those working on no-code tools are the ones who are closest to achieving the goal of “everyone without prior knowledge.” These simple machine learning platforms provide a compelling case for the time/value/knowledge trade-off, allowing users with no prior knowledge of AI coding to improve day-to-day operations and address business challenges.
Where to Start with No-Code
A competent no-code platform needs three critical features.
- First and foremost, it requires a user-friendly interface that makes it straightforward to enter data into the model training process. This entails connecting with today’s major corporate systems, such as CRM systems like Salesforce and spreadsheet tools like Excel. The platform should be able to combine relevant data from numerous sources.
- Once the data is uploaded, the platform needs to be able to automatically classify and correctly encode the data for the model training process — all with minimal input from the user. For example, the platform might identify columns in the data like categories, dates, or numbers, and the user should check to see that the columns are labeled correctly.
- The platform must then automate model selection and training, which are operations typically undertaken by data scientists. There are numerous machine-learning approaches, each of which is best suited to a given situation. The platform should have a search function for locating the optimal model based on the data and the desired prediction. There should be no need for the user to be familiar with regression or k-nearest neighbor methods. The platform should only deliver what is most effective.
Finally, it must be straightforward to integrate into existing procedures. As the business environment changes and new data becomes available, a platform should be able to track model performance over time and retrain as needed.
Traditional ML vs. No-Code ML
Nowadays, most AI programs follow the same strategy. Indeed, you begin by
- Selecting a use case
- Collecting data,
- Building a model,
- Training and improving it, among other things.
We must first grasp the distinctions between “conventional” and “no-code” machine learning to evaluate if no-code AI can help.
In a “traditional” AI project, drag-and-drop tools can automate or simplify various tasks. Furthermore, we see no-code AI platforms as a fast way to create prototypes and demos.
The Benefits of No-Code AI
There are various benefits of using no-code. In addition to the conventional “easy and convenient,”
- Maintenance is easy
As a result, the speed with which an application can be created has risen and has even become simple. The IT department is no longer inundated with requests. Tasks that once took months to perform are now completed in hours or days.
- Productivity gains
The speed with which an application can be produced has increased and has even become simple as a result of this. The IT department isn’t swamped with inquiries anymore. Furthermore, tasks that used to take months to complete are now completed in hours or days.
- Changeable
Traditional coding has the drawback of making it difficult to update functionality, especially if the code is written in a language you are new to. You can quickly modify the functionality with no code in only a few hours.
Examples of No-Code Platforms
Here’s a selection of no-code platforms to consider if you need to deploy a machine learning component and integrate it with an existing program
- Google Cloud Auto ML: It allows you to design your own bespoke machine learning models using Google’s machine learning capabilities.
- BigML: It offers business analysts and application integration with commoditized machine learning as a service.
- CreateML: It accomplishes tasks such as picture recognition, text extraction, and numerical value relationship discovery.
- DataRobot: It aids in the preparation, development, deployment, monitoring, and maintenance of enterprise-scale AI applications.
They are not a viable alternative to specialized ML model creation in high-load, data-intensive circumstances. These technologies are interesting for what they are: no-code platforms that allow non-technical users or ML beginners to create simple apps quickly.
Final thoughts
The goal of no-code learning is to assist businesses in achieving goals, such as turning data into actionable insights through predictive analytics in a matter of minutes. From rapid deployment to plug-and-play integration, platforms may be created from the ground up with end-to-end scalability in mind.
It is no surprise that Google, Facebook, Microsoft, and Amazon have all prioritized AI and machine learning in their innovation agendas.
As a result of democratizing access to machine learning capabilities across any team — regardless of skill level — more firms will recognize the promise of AI. Let us start embracing the future as soon as we can.
