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"How can I start building my own generative AI model from scratch: What are the required skills and resources?"
Generative AI models have traditionally been a major undertaking, accessible only to well-resourced tech heavyweights with billions of dollars in funding.
However, with advancements in technology and the availability of resources, it is now possible for others to build their own generative AI models.
Building a generative AI model involves developing a foundational model using institutional data, which includes millions of images, sentences, or sounds, etc.
To train a generative model, we collect a large amount of data in a specific domain and then train a model to generate data similar to the training data.
There are three main avenues for fine-tuning generative AI models: fine-tuning, retrieval-augmented generation (RAG), and prompt engineering.
A generative AI system is one that learns to generate more objects that look like the data it was trained on.
Generative AI can learn from existing artifacts to generate new realistic artifacts at scale that reflect the characteristics of the training data.
Generative AI uses a number of techniques, including deep learning models, to produce a variety of novel content such as images, video, music, speech, text, software code, and product designs.
Building a separate tech stack for generative AI creates more complexities than it solves, making it essential to integrate it with existing systems and data.
To build a generative AI model, one needs to choose a specific type of model, such as a generative adversarial network (GAN) or a variational autoencoder (VAE), and then train the model to generate data similar to the training data.
Generative AI can be thought of as a machine-learning model that is trained to create new data rather than making a prediction about a specific dataset.
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