Online applications such as machine learning and other artificial intelligence (AI) applications have changed the world in many ways, yet most people perceive programming as a complex and difficult task. However, it is important to understand that you do not necessarily need to be a programmer to implement AI. AI models can be trained using user-friendly platforms and no-code tools. In this guide, we will focus on the basics of training an AI model, focusing on the no-code interface.
This guide is not only for IT professionals, coders, and developers interested in tinkering with algorithms. It is aimed at a wide range of enthusiasts, including executives looking to streamline their business processes, marketers looking to better understand their customers, and anyone who has ever wondered how AI works. Upon completing this course, you will learn how to build, train, and implement customized AI models using a simple graphic interface, allowing you to bring AI into your work without the need for program code. Get started right away and learn how easy and revolutionary AI can be, especially if you are a beginner who is completely new to the concept of AI.
Understanding No-Code AI
No-code AI platforms allow you to build applications that integrate artificial intelligence without using any code. Most of them have a user-friendly setup where users input data, select the required algorithms, and train the model using icons and navigation menus. These platforms are ideal for individuals or companies that are interested in benefiting from artificial intelligence systems but do not have sufficient funds to set up an artificial intelligence department.
Popular no-code AI tools
1. Machines that can learn
Overview: Teachable Machine is a fascinating technology developed by Google for building image, speech, and pose detection models.
Features: Easy to use with drag-and-drop format, training as you go, and instant application activation.
Pros: Mainly suitable for presentation purposes and small scale production needs.
2. Robe
Summary: Acquired by Microsoft this year, Lobe is a deep learning modeling tool designed with a graphical front end.
Features: Suitable for image classification, fast training algorithmic models, and compatibility with other tools.
Pros: Perfect for individuals looking for an easy way to integrate this aspect of artificial intelligence.
3. Data Robot
What it is: DataRobot provides end-to-end intelligence for building, deploying, and managing intelligent AI models.
Capabilities: Fully automated end-to-end machine learning with an AutoML service, comprehensive data visualization solutions, and improved model explainability.
Pros: Perfect for companies that are interested in implementing artificial intelligence into their business but can’t afford to hire experts to write code.
4. Runway ML
Summary: Runway ML is also aimed at new and potentially creative pursuits, allowing users to create art, videos, and more through artificial intelligence.
Features: Ability to train models to perform multiple creative tasks, intuitive user interface, online collaboration during the process.
Pros: Perfect for artists, designers, and content developers looking to add AI to their artistic and creative work.
5. Big ML
Overview: BigML is a single platform that provides a set of tools for creating and deploying machine learning models.
Features: Intuitive dashboard, micro-automation of analytical processes, almost complete flexibility of models.
Benefits: For data science and business analytics practitioners who need powerful, scalable AI tools.
Below are some techniques for training AI models without writing any code:
1. Since a no-code AI platform will be the foundation for all your development, we highly recommend choosing it carefully.
Choose a platform to run on based on your persona: Teachable Machine and Lobe are good for people who want to start with on-device learning tools.
2. Prepare your data
Collect and prepare your data. Label it properly and explain all the points you want your audience to understand about the problem you are addressing. Data quality is important, especially if you are feeding it into a model that is being trained to help with decision making.
3. Upload your data
Perform analysis based on the data imported into your platform of choice. Most no-code AI tools offer input and output options for various data formats such as CSV, images, audio, etc. Ensure that your data files are formatted correctly depending on the end use of your social media platform.
4. Select the model type
Choose the type of model you want to train. For example, you can decide to work on image classification for instance identification in photos or sentiment analysis if you are processing text data. The algorithms and all the parameters required for your selection are pre-populated to help you make the right decision.
5. Train the model
Training a model requires following the guidance provided by the platform, which often involves adjusting various settings, choosing the right algorithm to use to train the model, and the training process. The platform displays a representation of the training model and provides feedback on the same during training.
6. Evaluate the model
When evaluating your model, use standard software testing metrics that are integrated into your programming language. Examine categories such as absolute error, precision, and recall. Most platforms provide tools to help you evaluate such values, so you can stay informed of the health of your model.
7. Deploy the model
Once this is done, you can start using your model to make predictions. The steps above are the general process of using a machine learning model to make predictions. Therefore, traditional no-code platforms often provide easy deployment solutions, such as creating an API interface or using the platform with your existing applications, which allows you to easily put your model into practice in real time.
The Benefits of No-Code AI
Accessibility: Enable individuals with basic IT literacy to contribute to AI development.
Speed: No coding and high creativity encourages rapid development and deployment, allowing for faster iteration.
Below are some great use cases for no-code AI:
1. Educational Projects
Leveraging no-code AI in the teaching-learning process allows teachers and students to use AI to design projects, improve the content delivery process, and enhance hands-on project experience as part of knowledge acquisition.
2. Solutions for Small and Medium-sized Enterprises
Even small and medium-sized businesses can now harness the potential of AI to transform their operations, from automating tedious processes to making better decisions through insightful data analysis, without the need for specialized IT skills or resources.
3. Creative Initiatives
AI will have a huge impact and benefit artists, designers, and content creators as a way to develop new creative ideas and grow their work.
4. Prototype and Experiments
Startups and business people can leverage AI as a tool to rapidly generate and demonstrate ideas to assess the profitability of potential products before moving forward with the implementation process.
5. Healthcare and research
Finally, artificial intelligence can help researchers and health professionals develop data literacy, draw logical and accurate conclusions, and apply them in practice to increase the effectiveness of interventions.
