Low-Code vs No-Code Computer Vision Platforms

As the enthusiasm for miraculous AI solutions booms louder, more and more enterprises without advanced Python development teams (or small teams) are looking for ways to implement impactful solutions. Beyond Generative AI, companies are increasingly looking to Vision AI to harness the potential of our most powerful sense. Further, enterprises compare the advantages of low-code versus no-code platforms.

Vision AI, also known as computer vision, is a complex but incredibly useful tool that offers enterprises real-time, visual data in their operations. Computer vision leverages existing cameras to detect people, objects, and events, acting as a set of eyes overseeing your operations even when humans aren’t looking. 

Training Vision AI models and building applications takes time and advanced technical know-how. However, working with an established computer vision platform – with all the tools necessary to build and deploy these models – can significantly reduce the barrier to entry. Most of these platforms are either no code or low code.

No code Vision AI platforms promise the ability to build AI applications without writing a single line of code. Sounds so simple, easy, and doable – and if it can also deliver results? Yes, please! And nothing inspires us results-focused, non-developer folks than a “no code” label. It’s easy to see why they’ve sparked excitement among enterprises looking for quick ways to deliver powerful insights. 

But is the lure of no-code AI too good to be true? While it’s true no-code solutions are accessible, they can also have limited functionality and may not truly address your pain points. Using a low-code platform can offer more flexibility and greater tailoring to better meet your specific needs.

What Is a No-Code Computer Vision Platform? 

A no-code computer vision platform is a tool that allows you to build computer vision applications without writing lines of code making it easy for anyone to implement. No-code platforms are important in democratizing AI and creating opportunities for any business to implement this transformative technology. 

No-code computer vision platforms can vary but they generally use visually intuitive, pre-built components to simplify each step of building an operational application. This is especially useful for the more complex aspects of computer vision, like model training and application development. Because no-code platforms use simple, pre-configured components, prototyping and deploying them is faster than with other more advanced platforms. 

While these benefits make no-code computer vision platforms a great option for some enterprises, they are not without their drawbacks – namely limited customizations and functionality. No-code platforms are great for getting started but limit how far enterprises can take the technology. 

What Is a Low-Code Computer Vision Platform?

A low-code computer vision platform is similar to no-code in that it does not require advanced coding knowledge and uses pre-built components and an intuitive interface. Low-code platforms do not require coding from scratch but provide a jump start by simplifying the most complex parts of coding. Like no-code platforms, low-code platforms are great for beginners but also offer options for Python developers to add custom code snippets for fine-tuning specific functions. 

The primary difference between the two types of platforms is the level of customizations you can make to accurately address your pain points. This difference is most notable in application (and in some cases model) development.

With low-code platforms, anyone with basic knowledge of Python (or similar coding language) can quickly adjust and improve applications. More experienced developers can then move on to creating other AI projects or focusing on other tasks. Therefore, low-code platforms allow for more flexibility and appeal to beginner and advanced users. 

Because they share some of the same characteristics, no-code and low-code platforms are often lumped together against full-code options. However, their differences are distinct. 

Specificity vs Simplicity

No-code platforms excel in simplicity. Pre-built templates and drag-and-drop interfaces make them beginner-friendly. But this ease comes at a cost: limitations.

No-code platforms, by design, cater to a broad audience. This means they offer a restricted set of features and functionalities. A no-code platform is ideal for straightforward or simple use cases. For example, if you wanted to count people in an area with a static background, consistent (and good) lighting, with no obvious obstructions in the view, an out-of-the-box people-counting application would work. 

No-Code Limitations

But for most real-world use cases, a no-code platform can't provide the tailoring necessary to meet the dynamic needs of most enterprises.  

Many no-code platforms have limited flexibility. Users choose pre-defined options without understanding the underlying algorithms or logic. This lack of transparency hinders customization and troubleshooting, making fine-tuning for optimal results almost impossible.

Anytime there is a physical change to your location (i.e. if furniture or equipment moves), products, or processes, you likely need to make changes to your computer vision application. However, with no-code platforms, you might be limited in what changes you can make. 

Low-Code Limitations

Low-code, on the other hand, balances power and functionality with simplicity bridging the gap between no-code's user-friendliness and traditional coding's power. 

Enterprise environments rapidly change. There are, of course, physical changes to office or shop floors, but businesses also generate new data, discover new applications, or develop new features. With a low-code platform, businesses can tweak and retrain an existing model or application easily.

This often includes using a few lines of code and likely requires some knowledge of a programming language like Python. The ability to tinker with models and applications means the resulting capabilities can be more sophisticated. With low-code platforms, organizations can create complex applications to tackle real-world problems. No-code platforms with simple applications can only target simple problems. 

Low-code platforms are also more likely to handle system integrations more easily. Businesses use a variety of digital and analytics tools regardless of industry so it’s understandable that adding more tech to an already full stack can feel daunting. However, low-code platforms are generally more easily integrated into other BI tools and dashboards whereas no-code platforms often function in a silo. As a result, organizations can get locked into the platform.

How Do I Know Which to Choose?

Knowing whether you can get by with a no-code solution or if you should invest in a low-code platform can be difficult. Before you jump in, here are some questions to consider:

• What problem are you trying to solve? Do you need computer vision to detect something small or large (i.e. a defect on a small bottle or a dump truck)? Do you need to detect objects in motion?
  
• What is my Vision AI budget?
  
• Do you have an internal team of developers? What programming languages do they know?
  
• Does the platform have tools to take your project from the first step of data collection and annotation through the deployment and maintenance of your applications? Some no-code platforms may provide only limited capabilities.
  
• What are your security requirements? If security is a main priority for your enterprise, low-code platforms may be a better option. Some no-code platforms may fail to design secure access protocols. Because users may not truly understand the mechanics behind the applications they develop, they might unknowingly expose sensitive data.

Understanding your use case and the realities of Vision AI technology are critical first steps to ensuring success. The more complex your use case or the more variable your environment is the more you want a flexible platform that can adjust to your evolving needs.

Are There Other Alternatives?

Thanks to the growing popularity of AI technology, there are options beyond no-code and low-code platforms. This can include computer vision consulting services. In these instances, an established computer vision provider assists with model and application development depending on the support you need. 

Or, you can opt for full-service computer vision providers. In this case, the computer vision provider would work with your team to understand your needs and deliver a fully operational application. While this option may seem more expensive up front, it can often pay dividends as your business scales its computer vision solutions.

A third option would be to do it yourself. This would require an experienced development team to find open-source tools and build the entire project from the ground up (an arguably arduous task). 

Choosing the Right Tool for the Job

No-code and low-code each have their place. No-code shines for simple tasks and quick prototypes. But for the majority of real-world applications, low-code is the clear winner. With the right platform and approach, low-code empowers individuals and businesses of all technical backgrounds to harness the power of computer vision and unlock its full potential allowing you to truly customize, control, and innovate in the exciting world of Vision AI.