Custom Models Tutorial

Welcome to the tutorial on creating custom models using our AI Kit. In this guide, we'll walk you through the process of building, training, and deploying your own AI models.

Prerequisites

Before you begin, make sure you have the following prerequisites:

Basic knowledge of machine learning and deep learning concepts
Familiarity with Python programming
Access to a computer with a suitable development environment

Step-by-Step Guide

Step 1: Data Preparation

First, you need to gather and prepare your data. This can be a dataset from a public repository or your own collected data. Ensure that your data is clean and well-structured.

Data Preparation Best Practices

Step 2: Model Selection

Next, choose the appropriate model architecture for your task. Our AI Kit provides various pre-trained models that you can use as a starting point.

Model Architecture Guide

Step 3: Training the Model

Once you have selected a model, it's time to train it using your data. We provide a simple and intuitive API for model training.

Model Training API Documentation

Step 4: Model Evaluation

After training, it's crucial to evaluate the performance of your model. This helps you understand how well your model is performing and where it may need improvement.

Model Evaluation Techniques

Step 5: Deployment

Finally, deploy your trained model to a production environment where it can be used to make predictions or perform tasks.

Model Deployment Guide

Additional Resources

For further learning and support, we recommend the following resources: