Coding is a must when it comes to model deployment. Codeless ML solutions reduce entry barriers and encourages to experiment with machine learning. Over the years, tech-giants are turning open-source and developing newer tools which can integrate different types of data from multiple platforms and provide insights. Thus, requiring less or no coding at some stages.
Benefits of No-code AI
As mentioned earlier, AI solutions requires coding, data categorization, data cleansing, model training and testing. The main objective of a codeless solution is democratization of AI for their target users (who are many of the times are regular engineers). No-code AI solutions help in reducing the development time and encourages businesses to adopt AI-based models at a low-cost. This encourages non-technical users to build applications, ML models without having to program in a conventional way.
Some tools which support Codeless ML
MakeML uses different vision techniques which can help in image recognition and analysis.
Clarifai is an NLP tool which offers end-to-end solution for the entire modeling life-cycle. It has a comparatively faster API and some neat pre-trained models for its functionality.
DataRobot platform is a prominent name in this segment. Powered by open-source algorithms, it’s focus is on predictive models.
Nanonets falls under the domain of Computer Vision. One of the unique solutions which they offer is: building an ID card verification model. This also handles perspective transformation meaning, the model will also work with an angled or tilted image.
RunwayML. This tool is more of a storytelling machine that can generate images on-the-go as we write something. It supports motion capture, images, videos, background removal, object detection as well as style transfer.
Teachable Machine is a web-based Google tool which classifies images, sound and body postures. The machine can be taught using a webcam, or a live browser. It can run a model with 30 images per class.
Another one in this list is the What-If-Tool which is mainly used for comparison purpose. It maintains transparency while comparing two models. Confusion matrices and ROC curves are used to check precision of the models.
No-code ML vendors are present in the market based on the type of technology they provide, such as computer vision, voice recognition, motion capture or NLP. Businesses are gradually moving towards these platforms. Firstly, due to management of work-forces. Secondly, to make the utility accessible to everyone. Application development is approaching towards low-code or no-code AI, thus their demand will be increasing overtime.
