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3. Build your model: Use intuitive, drag-and-drop interfaces to create models for tasks like classification,
regression, or clustering. The platform will guide you through the process and offer suggestions based on best
practices.
4. Train the model: Once the model is built, use the platform’s Auto-ML (Automated Machine Learning) features
to optimise the training process. These tools automatically select the best algorithms and parameters for the
task.
5. Fine-tune parameters: After initial training, adjust model parameters to improve performance. No-code
platforms provide automated suggestions or allow manual fine-tuning for further optimisation.
6. Evaluate and refine: Assess the model’s performance using built-in metrics. Interpret the results through
visualizations, and make necessary adjustments to enhance accuracy and efficiency.
7. Deploy the model: Finally, deploy your model with ease. No-code tools provide deployment options like APIs,
web services, or seamless integration with your existing systems, making the deployment process simple.
Introduction to Lobe
Lobe is a user-friendly, no-code AI tool designed to make it easy for anyone to create AI models without needing
programming skills. It is part of a category called Automated Machine Learning (Auto-ML) tools, which handle the
complex processes of building, training, and optimising AI models automatically.
Lobe specialises in image classification, a type of AI task where the goal is to identify and categorise objects
within images.
• It provides images with labels: You start by uploading a set of images to Lobe. Each image should be labelled
to indicate what it contains. For example:
An image of a cat is labelled as "cat."
An image of a dog is labelled as "dog."
• Automated model creation: Once you upload and label your images, Lobe automatically takes care of the
technical work:
✶ It analyses the images.
✶ It tries different AI models to find the best one.
✶ It optimises the model for the highest accuracy in classifying your images.
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