Teachable Machine AI

Teachable Machine is a machine learning tool developed by Google that allows users to create machine learning models without any coding. The platform provides a graphical user interface for training and exporting models that can be used in various applications.

With this technology, users can build models to classify images, audio, or even poses. After doing the same, users can download the trained model and use it for their applications. The platform is designed to make creating machine learning models fast, easy, and accessible to everyone.

Teachable Machine 2.0, the latest version of the platform, lets users train their own machine learning model with the click of a button, no coding required, and export it to websites, apps, physical machines, and more. The platform can also recognize sounds and poses, like whether you’re standing or sitting down.

Teachable Machine AI Features

• AI-powered image recognition: The tool can recognize images and classify them into different categories. This feature can be used for tasks such as identifying objects in images, detecting faces, and more.

• Sound classification: This tool can also classify sounds into different categories. This feature can be used for tasks such as detecting different types of bird calls, identifying musical instruments, and more.

• Pose detection: It can detect human poses in real-time. This feature can be used for tasks such as tracking the movements of athletes, monitoring the posture of office workers, and more.

• Easy-to-use interface: The tool has a simple and intuitive interface that makes it easy for beginners to get started with machine learning.

• Export models: Users can export their trained models to use in other applications such as websites, mobile apps, and more.

Teachable machine AI Pricing 

It is a free-to-use platform.

Teachable machine Usages

• Object detection: You can teach the computer to recognize and locate different objects in an image or video, such as faces, cars, animals, etc. For example, you can create a model that can detect and count the number of people in a crowd.

• Gesture recognition: You can teach the computer to recognize and interpret different hand gestures, such as waving, pointing, thumbs up, etc. For example, you can create a model that can control a game or an app with your hand movements.

• Speech recognition: You can teach the computer to recognize and transcribe different words or phrases spoken by you or others. For example, you can create a model that can convert your voice to text or translate it to another language.

Competitors Teachable machine AI

• Julius: A tool that lets you analyze and visualize any form of structured data using natural language prompts. You can link data sources, generate visualizations, perform advanced analysis, and export or share your results with confidence.

• Vfitter: A service that helps you monetize your digital creations, such as audio, video, or interactive experiences. You can create custom products, bundles, and subscriptions and sell them to your fans or collaborators.

• Brancher: A platform that lets you create AI-powered apps in minutes, with no code, by connecting AI models to your app. You can choose from over 100 templates, get 100 credits for free, and monetize your app creations with no code.

Launch and Funding Teachable machine AI

This tool was launched by Google in 2017 and runs on top of TensorFlow.js12. It is a project by Google Creative Lab. The director of Google Creative Lab is Steve Vranakis, who oversees various creative and experimental projects at Google.

Limitations Teachable machine AI

• Data Type Restriction: The platform solely supports image, sound, and pose data for machine learning models, excluding text, video, or numerical data.

• Customization Constraints: Users face limited options for customizing models, lacking choices in architecture, hyperparameters, and loss functions, and relying on default settings.

• Export Limitation: Model export is confined to TensorFlow.js, restricting users from exporting to other formats or platforms such as Python, TensorFlow Lite, or PyTorch.

• Evaluation Gap: The website lacks built-in metrics for users to assess model performance, requiring manual testing or external tools for quality evaluation.