What Is Generative Modeling?

Generative modeling is the process of discovering hidden patterns and relationships in a given dataset to generate new but statistically similar data. Deep generative models are deep learning algorithms that can learn any dataset, and then generate new data samples that closely match the original dataset. For example, a deep generative model can process many images of faces and then create a new and unique facial image not found in the original dataset.

Discriminative modeling uses probabilistic distribution to identify and classify data into its respective categories. A discriminative model learns from data samples to understand the distinct features of a particular object. Then, it applies the learned knowledge to identify similar objects in real-world data. In contrast, generative modeling uses deep neural networks to understand the underlying probability distribution of training samples to generate new data points.

For example, data scientists use analogous discriminative models to detect whether there are people in an image. On the other hand, discriminative models can generate realistic images of people who don't exist in real life. While discriminative and generative models serve different purposes, they play important roles in innovating and automating business applications.

Generative modeling revolutionizes the way humans interact with technology and experience content. It leverages the massive data we have with machine learning models to innovate enterprise workflows in different ways. We give some benefits below.

Personalize user experience

Deep generative modeling can learn from vast consumer data and extract useful insights to enhance customer journeys. For example, chatbots powered by generative models can detect underlying patterns from customer conversations and recommend products that interest them.

Optimize business workflows

Generative AI technologies allow you to streamline business processes across different departments. With generative models, you can turn observed data into actionable insights to improve business outcomes. For example, operation managers can identify bottlenecks in manufacturing facilities by feeding machine-generated data into generative models. They can also use production data to plan inventory movement in upcoming months.

Improve productivity

Generative artificial intelligence can automate workflows that help companies save time and expenses usually exhausted on redundant or manual tasks. For example, generative AI can analyze large datasets to predict trends, generate reports, or suggest decision-making strategies. This not only speeds up the analysis process but also provides deeper insights, which can be crucial for business strategy and operational efficiency.

Explore new ideas

Generative models are tools that assist in creative works. They save designers, copywriters, marketers, and other creative professionals time and expand their idea pools by generating relevant suggestions in seconds. For example, you can ask generative AI chatbots to list catchy taglines for an ad campaign, which you can improve later.

Generative modeling works by producing foundational models capable of generating artificial data that resembles genuine data. Foundational models are deep learning models that train on large volumes of information. These models are trained with unsupervised learning, which feeds a deep generative model with unlabeled text, images, audio, or other data. Once trained, you can use the models to create new and realistic data by giving specific instructions. However, foundational models produce generic results and need further refinement before being used for your business. We explain the stages of training, fine-tuning, and applying generative models to business-specific applications below.

Training generative models

In unsupervised learning, data scientists feed the model with large volumes of unlabeled curated data. The model then learns to recognize hidden patterns and predict sequences from the training data. For example, a large language model learns to complete sentences during training. From there, it can reproduce the linguistic style that people use in everyday conversation.

Fine-tuning foundational models

A trained large generative model can generate new data instances but is limited to the scope of knowledge it learns from. When presented with domain-specific requests, it can't generate accurate and relevant results. With fine-tuning and reinforcement learning, the foundational model refines its learned data distribution with small but specific data related to a particular topic. For example, you can fine-tune a foundational model with product guides to develop a generative AI chatbot for your internal knowledge base.

Applying generative modeling

You can integrate fine-tuned generative models with software applications to automate and improve business workflows. These models are now more helpful and can generate accurate data when fed with information exclusive to your business.

For example, Adidas fine-tuned a generative model hosted on Amazon Bedrock to allow its engineers to answer technical questions from its internal knowledge base quickly.

We share several deep generative models that can create new data that closely matches the samples they train on.

Transformers

Transformers are large language models with exceptional natural language processing capabilities. These models learn from textual data curated from the web, allowing them to write essays, answer questions, program codes, and more. Transformers can also process input data in parallel, enabling them to handle large amounts of information more effortlessly.

Generative adversarial networks

Generative adversarial networks (GANs) use a generator and discriminator to produce new data that humans pass as genuine. The generator produces data, which the discriminator checks to decide if it's real or fake. Eventually, the generator gets better at generating realistic data that could fool the discriminator—and humans. Because of that, the generative adversarial network is helpful for image generation.

Variational autoencoders

Variational autoencoders (VAEs) compress input data into a compact representation of its original form. Then, they sample from the stored data distribution to create data that resembles the existing data they learned from. Like GANs, VAEs are commonly used for image generation.

Pixel recurrent neural networks

Pixel recurrent neural networks (PixelRNNs) apply conditional distribution to predict the next pixel from previous pixel sequences. They analyze each pixel that precedes the missing pixels and forecast the most likely color that completes the image.

Diffusion model

The diffusion model is ideal for creating unique artificial intelligence (AI)-generated images. The model learns by applying random noise to a sample image, which it later denoises to recover the original form. Unlike GANs, the diffusion model doesn't need an adversarial network to produce highly realistic generated data.

Bayesian network

Bayesian networks are statistical models that can compute the probabilistic value of specific events based on past precedence. In generative modeling, Bayesian networks are helpful in medical applications, such as contact tracing, epidemic control, and clinical diagnosis.

Restricted Boltzmann machines

Restricted Boltzmann machines (RBMs) consist of visible and hidden layers where neurons extract features and use them to generate new samples. They power various applications, including speech recognition, computer vision, and language processing.

Amazon Web Services (AWS) makes it easy to build and scale deep generative models for your data, use cases, and customers. With generative AI on AWS, you get enterprise-grade security and privacy, access to industry-leading foundation models (FMs), and a data-first approach. For example:

• Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs and a broad set of capabilities. You can easily experiment with various top FMs, privately customize them with your data, and create managed agents that execute complex business tasks.

• Amazon SageMaker JumpStart provides managed infrastructure and tools to accelerate scalable, reliable, and secure model building, training, and deployment. You can use it to discover, explore, and deploy open-source FMs—or even create your own.

Get started with generative modeling on AWS by creating a free account today.