How to Build Your First AI Model: A Beginner’s Guide to Training Custom AI Systems

For the average user, artificial intelligence often appears as a black box—pre-packaged tools like video upscalers or image generators that deliver impressive results with a single click. But for those seeking to build their own AI models from scratch, the journey is far more intricate. A recent Reddit post from a novice enthusiast, /u/BiscottiAsleep2991, asking how to create an AI model for video filtering, underscores a growing curiosity among non-technical users to move beyond using AI and begin shaping it. While the path is challenging, it is not inaccessible—with the right guidance, foundational knowledge, and persistence, even beginners can begin to navigate the landscape of AI model development.

Contrary to popular belief, creating an AI model is not as simple as signing up for a service like Gmail or launching a survey. According to Google’s official documentation on account creation and survey design, these are user-facing, low-code interfaces designed for accessibility. Building an AI model, however, requires a fundamentally different approach: one rooted in programming, data science, and computational resources. Unlike creating a Gmail account—which merely involves filling out a form—or designing a Google Survey—which relies on point-and-click question builders—training an AI model demands writing code, sourcing and cleaning datasets, selecting appropriate neural network architectures, and deploying training environments often requiring high-performance GPUs.

For someone interested in video restoration or filtering, as the Reddit user described, the most common entry point is using PyTorch or TensorFlow, open-source machine learning frameworks that power many of the models shared on platforms like OpenModelDB. These models are typically trained on thousands, if not millions, of video frames to learn patterns of noise, compression artifacts, or low resolution. The beginner must first acquire a dataset of paired low-quality and high-quality video samples, preprocess them into consistent formats, and then define a convolutional neural network (CNN) or a transformer-based architecture capable of mapping low-resolution inputs to high-resolution outputs.

Training such a model requires substantial computational power. Most hobbyists use cloud services like Google Colab, which provides free access to GPU resources, or rent time on platforms like AWS or Lambda Labs. Without adequate hardware, training can take days or weeks, and even then, results may be subpar without fine-tuning. The variability in outcomes—something the user noted when testing different models—is often due to differences in training data quality, hyperparameter settings, and the model’s architecture. A model trained on anime footage will perform poorly on real-world CCTV footage, highlighting the critical importance of domain-specific training.

Moreover, ethical and legal considerations loom large. Many video datasets used for training contain copyrighted material. Developers must ensure compliance with fair use policies or use publicly licensed datasets such as those from the YouTube-8M or Vimeo-90K projects. Open-source communities like Hugging Face and GitHub have begun curating ethical, legally compliant datasets to help newcomers avoid infringement.

For the aspiring AI developer, the journey begins not with a click, but with a commitment to learning. Resources like PyTorch’s official tutorials, Coursera’s Deep Learning Specialization, and YouTube channels such as ‘3Blue1Brown’ provide accessible entry points. The path from beginner to model creator is long, but with structured learning, patience, and iterative experimentation, it is entirely achievable. As AI becomes more democratized, the next generation of innovators won’t just use models—they’ll build them.