How to build an ai platform with no code

No-code AI is a type in the AI application that aims to democratize AI. No-code AI means using a no-code development platform with a visual, code-free and often drag-and-drop interface to deploy AI and machine learning models. No code AI enables non-technical users to quickly classify, analyze data and easily build accurate models to make predictions without business analysts.

No code AI Platform is the closest to the ideal of “any person without prior training” when it comes to data handling. The increased level of democratization of data to personalized applications has been proven time and time again, the majority of businesses struggle to implement AI to utilize its full potential and scale the use cases to all workflows of the company.

With each model of ML built for a company, the goals of the unique model of No-code would be to conquer complexities, increase efficiency with automated machine learning, instant visualizations and Auto Feature Engineering which all provide ready results once the AI platform is up and running.

Here is how to build an AI platform with No-code


Building custom AI solutions requires integrating with an existing data source or uploading your data files in just a few clicks, cleaning data, categorizing, structuring data, training and debugging the model. These take even less time on No-code to enable Auto Data Preparation. Studies claim that low code/ no-code solutions have the potential to reduce the development time up by 90%, so connecting your existing business models and data with point and click interfaces will draw the most benefits once employed.


No-code AI platforms allow automation through drag and drop UI. Easy-to-use AI platforms leverage the time/value/knowledge trade-off in a genuinely attractive way and allow customers with no AI coding skills to optimize day-to-day operations and to solve business issues.

Visual, often drag-and-drop, no-code AI tools make AI easy to operate and build a personalized platform for non-technical people or those who lack the time or resources to build such systems from the ground up. Building the No-code allows skipping training, testing and validating the models that are traditionally required by ML platforms.


Saving and launching takes just a few clicks and can be instantly applied on web platforms or other applications to start using on your business models.


Inviting and corporating relevant data and integrating AI models help the ML make a better prediction and help with accuracy in all applications.

Besides the ease of starting with No-code, there are some huge advantages to no-code AI:

  • Accessibility: No-code AI allows businesses to make use of AI in the first place and can act as the stepping-stone towards intensified use of data science or AI in the future. The relatively low investment paired with people building up hands-on knowledge of AI tools tackles the biggest obstacles to AI adoption at small and mid-sized companies.
  • Usability: Drag-and-Drop allows anyone in the company to find an AI solution to a problem, and more often than not, in a budget-friendly way. These tools are built primarily with non-technical users and non-developers in mind.
  • Speed: The best type of no-code AI application allow users to iterate through time and help machine code platforms to learn quickly. This allows for more rapid experimentation to see what can be done using one’s data – and getting back to business right afterwards. The simplicity of the process of No-code deployment and intuitive processes help significantly in the launching speed.
  • Quality: No-code tools useful for people who may not have a technical talent group, to begin with. This takes significant work going into the product with defaults and safety measures that need to be carefully chosen on behalf of the user. To further tackle such risks, some AI platforms have human reviews built-in and ask for input when required. This reduces human error marginally when setting up such systems in the first place and allows direct interaction with the platform during daily operations.
  • Scalability: AI itself doesn’t have any drawback to manage a task for a single or a hundred users and neither do servers that are automatically scaled up or down, depending on the load.

How HyperSense AI Platform works?

HyperSense AI uses automated machine learning to automate iterative tasks of machine learning model development. It allows data scientists and experts to build ML models with higher scale, productivity, and efficiency while sustaining the model quality. It accelerates the time to get production-ready models with greater ease and efficiency. It also decreases human errors mainly because of manual measures in ML models. It also makes data intelligence accessible to all, enabling both trained and non-trained resources to rapidly build accurate and robust models, thus fostering a decentralized process.

The quality of a machine learning model is not only based on code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data. HyperSense AI Studio comes with features to store registered, discovered and used data as a part of an ML pipeline. It enables reusing features for different models driving AI at scale.

HyperSense AI Studio is built with assisted analytics capabilities, provides users with the ability to build applications with suitable levels of automation and human involvement at any stage of the data science cycle based on task and business requirements. It notifies users while creating a pipeline. It reduces complexity and iterations. These workflows are not a black box, so what happens at each stage can be surfaced to the user in detail, including the results.

Guided Analytics As many enterprises have started their AI journey and are at different stages of maturity, they require a module that can leverage their existing components in an organization while choosing a specific module suitable to their business needs.

HyperSense AI Studio is a highly composable data science studio that allows them to integrate with the existing assets in the organization. It can be integrated using various options such as APIs, files, databases, and streaming to achieve modularity and plug-and-play capabilities for quick data collection.

Composable Architecture Visual Analytics combined with HyperSense AI Studio’s automated ML capabilities provides users with visibility into how the AI system arrives at a decision and how the decision can be improved. Without AI Model Monitoring, sometimes the decisions made can be biased. Visual analytics can reduce these suspicions by providing a fine picture of the decision process. It improves the efficiency of the AI projects and helps to create ML pipelines quickly

HyperSense AI Studio provides ML operation capabilities that leverage automation to monitor, deploy and govern operations to manage machine learning lifecycle to get better business results. With ML platforms, models can be easily deployed into the production environments. It provides constant monitoring and production diagnostics to improve the performance of existing models. It also shows an AI trust and testing framework that helps to maintain the governance process for AI projects across the entire organization. ML HyperSense AI Studio supports multiple deployment options based on business requirements and priorities. The deployment options are On-premises Cloud or Hybrid.

Overall, deploying an AI Orchestration platform helps enterprises operationalize AI enabling scalability and growth. Facilitating technologies such as machine learning and AI assists with data preparation, model construction and deployment, insight generation, and insight explanation to augment how enterprises explore and analyze the data for substantial improvements.

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