What is a pretrained AI model?

Imagine trying to teach a toddler what a unicorn is. A good place to start might be by showing the child pictures of the creature and describing its unique characteristics.

Now imagine trying to teach an artificially intelligent machine what a unicorn is. Where would one even begin?

Pre-trained AI models offer a solution.

A pre-trained AI model is a deep learning model – a term for a brain-like neural algorithm that finds patterns or makes predictions based on data – that is trained on large data sets to perform a specific task. It can be used as is or further fine-tuned to suit the specific needs of an application.

Why are pre-trained AI models used?

Instead of building an AI model from scratch, developers can use pre-trained models and adapt them to meet their requirements.

To build an AI application, developers must first have an AI model that can perform a specific task, whether it’s identifying a mythical horse, detecting a security risk for an autonomous vehicle, or diagnosing a cancer based on medical imaging. That model requires a lot of representative data to learn from.

This learning process involves going through multiple layers of input data and highlighting target-relevant characteristics at each layer.

To create a model that can recognize a unicorn, for example, you can first feed it images of unicorns, horses, cats, tigers and other animals. This is the incoming data.

Then, layers of representative data features are constructed, starting with the simple—like lines and colors—and progressing to complex structural features. These characteristics are assigned different degrees of relevance by calculating probabilities.

Unlike a cat or tiger, for example, the more horse-like a creature appears, the more likely it is to be a unicorn. Such probability values ​​are stored in each neural network layer of the AI ​​model, and as layers are added, its understanding of the representation improves.

To create such a model from scratch, developers require huge datasets, often with billions of rows of data. These can be expensive and challenging to achieve, but compromising data can lead to poor model performance.

Precomputed probabilistic representations—known as weights—save time, money, and effort. A pretrained model is already built and trained with these weights.

Using a high-quality pretrained model with a large number of accurate representative weights leads to higher chances of success for AI implementation. Weights can be changed and more data can be added to the model to further customize or fine-tune it.

Building on pre-trained models, developers can create AI applications faster without having to worry about handling mountains of input data or computing probabilities for dense layers.

In other words, using a pre-trained AI model is like getting a dress or a shirt and then tailoring it to your needs, rather than starting with fabric, thread and needle.

Pre-trained AI models are often used for transfer learning and can be based on multiple model architecture types. A popular type of architecture is transformer modela neural network that learns context and meaning by tracing relationships in sequential data.

According to Alfredo Ramos, senior vice president of platform at the AI ​​company Clarifis — a Premier partner in NVIDIA Inception program for startups — pre-trained models can reduce AI application development time by up to a year and lead to hundreds of thousands of dollars in cost savings.

How do pre-trained models advance AI?

Since pre-trained models simplify and speed up AI development, many developers and companies use them to accelerate various AI use cases.

Top areas where pre-trained models are advancing artificial intelligence include:

  • Natural language processing. Pretrained models are used for translation, chatbots and other natural language processing applications. Large language models, often based on the architecture of the transformer model, is an extension of pretrained models. An example of a pre-trained LLM is NVIDIA NeMo Megatronone of the world’s largest AI models.
  • Speech AI. Pre-trained models can help speech AI applications plug and play across different languages. Use cases include call center automation, AI assistants and voice recognition technologies.
  • Computer vision. As in the unicorn example above, pre-trained models can help AI quickly recognize creatures – or objects, places and people. In this way, pretrained models accelerate computer visiongiving applications human-like vision capabilities across the board sport, smart cities and more.
  • Healthcare. For healthcare applications, pre-trained AI models such as MegaMolBART – a part of NVIDIA BioNeMo service and framework – can understand the language of chemistry and learn the relationships between atoms in real-world molecules, giving the scientific community a powerful tool for faster drug discovery.
  • Cyber ​​security. Pre-trained models provide a starting point for implementing AI-based cyber security solutions and extend the capabilities of human security analysts to detect threats faster. Examples include digital fingerprint of people and machines and detection of anomalies, sensitive information and phishing.
  • Art and creative workflows. Strengthens the latest wave of AI artcan pre-trained models help accelerate creative workflows through tools like GauGAN and NVIDIA canvas.

Pre-trained AI models can be applied across industries beyond these, as their customization and fine-tuning can lead to endless possibilities for use cases.

Where to find pre-trained AI models

Companies like Google, Meta, Microsoft, and NVIDIA are inventing cutting-edge model architectures and frameworks for building AI models.

These are sometimes published on model hubs or as open source, allowing developers to fine-tune pre-trained AI models, improve their accuracy, and expand model repositories.

NVIDIA NGC — a hub for GPU-optimized AI software, models, and Jupyter Notebook examples — includes pre-trained models as well as AI benchmarks and training recipes optimized for use with the NVIDIA AI platform.

NVIDIA AI Enterprise, a fully managed, secure, cloud-native suite of AI and data analytics software, includes pre-trained models without encryption. This enables developers and enterprises looking to integrate NVIDIA pre-trained models into their custom AI applications to see model weights and biases, improve explainability and debug easily.

Thousands of open source models are also available on hubs like GitHub, Hugs face and other.

It is important that pre-trained models are trained using ethical data that is transparent and explainable, respects privacy and is obtained with consent and without bias.

NVIDIA pre-trained AI models

To help more developers move AI from prototype to production, NVIDIA offers several pre-trained models that can be deployed out of the box, including:

  • NVIDIA SegFormera transformer model for simple, efficient, powerful semantic segmentation — available on GitHub.
  • NVIDIA’s custom-built computer vision modelstrained on millions of images for smart cities, parking management and other applications.
  • NVIDIA NeMo Megatronthe world’s largest user-definable language model, as part of NVIDIA NeMoan open source framework for building high-performance and flexible applications for conversational AItalk AI and biology.
  • NVIDIA StyleGAN, a style-based generator architecture for generative adversarial networks or GANs. It uses transfer learning to generate infinite paintings in a variety of styles.

Besides, Nvidia Rivaa GPU-accelerated software development kit for building and deploying speech AI applications, includes pre-trained models in ten languages.

And MONKSan open source AI framework for health research developed by NVIDIA and King’s College London, includes pre-trained models for medical imaging.

Learn more about NVIDIA pretrained AI models.

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