Generative artificial intelligence (AI) is poised to be a transformative force for businesses, projected to add at least $4 trillion to the global economy — and double that when considering generative AI embedded into software. Leading organizations recognize that they must be able to move their AI initiatives from mere proofs of concept (POCs) into production efficiently and cost-effectively. Having the right generative AI tech stack in place is a key part of a successful development and deployment strategy.
Read on to learn about key technologies required to build and run generative AI applications, how the tech stack differs depending on what type of generative AI you’re creating, and how to choose the right generative AI tech stack for your organization.
What is the generative AI tech stack?
Generative AI involves building models capable of creating content—such as images, text, or even entire pieces of code—autonomously. This is known as generative modeling, which relies on predictive algorithms to understand patterns and relationships within a given dataset to generate new information.
The generative AI tech stack refers to the collection of technologies and tools used to develop and deploy the generative artificial intelligence systems that create and run these models. Key components include the following:
Deep learning frameworks
The foundation of the generative AI tech stack is made up of deep learning frameworks, such as TensorFlow and PyTorch. These provide the infrastructure for building and training sophisticated models and implementing complex data manipulations, including generative adversarial networks (GANs), recurrent neural networks (RNNs), and variational autoencoders (VAEs), three popular architectures in generative AI.
Data preprocessing tools
Data preprocessing tools also play a crucial role in generative AI. These tools, such as Apache Spark and Apache Hadoop, are responsible for cleaning, organizing, and augmenting large and diverse datasets to train generative models effectively.
Programming languages
Programming languages are also required to develop and operate generative AI applications. The most common one used in machine learning is Python, due to its ease of use, large support library, and robust community support.
Deployment tools and platforms
Once a generative model is bult and trained, it needs to be deployed. Cloud-based platforms are typically used as they offer a combination of data flexibility, scalability, and system integration capabilities that generative AI applications require.
In general, complex generative AI initiatives require more extensive tech stacks with multiple frameworks and programming necessary to develop and support the applications.
Overview of generative AI tech stack layers
An effective generative tech stack is made up of three key layers: the infrastructure layer, the model layer, and the application layer.
The infrastructure layer
To develop and train AI models, organizations need the right mix of hardware, software, and cloud-computing tools able to support the significant demands of AI development. Hardware can include special graphics processing units (GPUs) that can power and train complex machine learning models, as well as chips specifically designed for AI operations, like Google’s Tensor Processing Units (TPUs). These components enable massive amounts of data to be processed quickly and efficiently.
Software elements like deep learning frameworks help developers build and train custom AI models to create POCs and accelerate generative development. Other software tools help preprocess, monitor, and visualize data and optimize models for deployment.
Cloud-based platforms deliver the storage and processing power necessary for complex AI development, training, and deployment processes. These platforms enable enterprises to scale AI applications efficiently and cost-effectively, which helps accelerate AI innovation and time to market.
The model layer
At the core of the generative AI tech stack rests the model layer. These models are trained on large amounts of data to recognize patterns and generate content. Organizations can build and deploy these models for specific use cases based on various business needs. Such models can either be developed in-house (proprietary) or developed from open-source generative AI frameworks. They can also be general or narrow in scope, such as specialized or hyperlocal models.
General models are designed to execute a diverse range of tasks and improve and learn over time. The more data they’re exposed to, the better they can recognize patterns, enhance performance, and efficiently generate solutions. This unlocks the potential to deliver more precise predictions and solve businesses’ most complex challenges.
Specialized models are designed for unique purposes and trained with specific data subsets to solve distinct problems, such as generating financial risk modeling, creating social media posts, or performing language translation. The narrower datasets allow these models to more deeply understand and interpret the data, so they can deliver greater precision, nuance, and accuracy.
The application layer
The application layer rests on top of the model and infrastructure layers. It helps make AI models user-friendly and accessible, unlocking innovation and solutions more quickly.
Organizations using proprietary models can use apps that integrate them into user-facing products, managing the entire AI pipeline, from data collection to deployment. These models can then be incorporated into software or other products and made available to consumers. ChatGPT is one example of this type of app.
Applications can also be made using non-proprietary technologies. These apps, made with open-source frameworks, make generative AI development available to more users and organizations, broadening the potential uses and benefits so more businesses can meet the specific needs of their customers.
Common generative AI tech stack models
Organizations may choose different generative AI tech stack models depending on the business needs they’re trying to address. Here are a few of the most popular:
General adversarial networks (GANs)
A general adversarial network or generative adversarial network consists of two neural networks: a generator and a discriminator. The generator creates synthetic data, like text or images from a given input. Then the discriminator evaluates whether an input is real (from the actual dataset) or fake (created by the generator). In the process, it learns to distinguish between real and synthetic data. As the name implies, the two networks have an adversarial relationship. They continuously compete against each other, as the generator attempts to develop increasingly realistic data and the discriminator tries to become better at discerning actual data from generated data. A GAN is most suitable for image, video, and text-to-image generation.
Recurrent neural networks (RNNs)
A recurrent neural network works by processing sequences of data one element at a time and maintaining a memory of previous inputs. By learning data patterns sequentially, RNNs can generate outputs that build upon themselves in a coherent way. This is especially useful for generative AI tasks like natural language processing, generative music, or speech recognition.
Variational autoencoders (VAEs)
Autoencoders are neural network architectures adept at capturing, reproducing, and enhancing input data. VAEs work by using an encoder and a decoder. The encoder creates a compressed version of the input data (latent space), and the decoder reconstructs and augments the input from that representation. VAEs can generate enhanced data samples from latent space, allowing users to create variations of the original input data. VAEs are commonly used for AI applications like image generation, drug development, and autonomous driving.
Generative AI application frameworks
Deep learning application frameworks streamline generative AI development by providing users with pre-built modules and other tools. These frameworks facilitate the building, training, and deployment of generative models. Commonly used ones include:
- PyTorch. PyTorch is an open-source deep-learning library largely used for developing generative models. Developed by Facebook, it’s known for rich, dynamic computational graphing, ease of use, and extensive community support.
- TensorFlow. Developed by Google, TensorFlow is a flexible, open-source framework that provides rich support for building a wide range of generative models, including generative adversarial networks, variational autoencoders, and more.
- Hugging Face Transformers. Built on top of PyTorch and TensorFlow, this library includes pre-trained models and easy-to-use interfaces aimed at NLP tasks like text generation.
- Keras. Keras is not a standalone framework, but it can be used with TensorFlow. It has a user-friendly, modular design that simplifies building, training, and deploying deep learning models.
- MXNet. Developed by Apache, this flexible, open-source deep learning library supports various generative models and is known for its scalability, making it a good candidate for both small-scale experiments and large-scale production deployment.
The business case for generative AI technology
Generative AI can be transformational for enterprises, accelerating innovation and maximizing opportunities for growth. Those that adopt generative AI technologies stand to gain a significant competitive edge now and in the future—unlocking insights faster, improving decision-making, and fast-tracking time to market. Here are just a few benefits of generative AI for businesses.
Faster product innovation
With its ability to automatically generate code, generative AI can significantly accelerate development efforts, increasing productivity and dramatically reducing operational costs. That can deliver innovative software, data products, and other offerings that accelerate business value.
Hyper-contextualized customer experiences
Generative AI can revolutionize comprehensive customer analytics, as well as product recommendation engines. By deepening the understanding of customer needs, behaviors, and expectations, businesses can respond with more authentic and relevant interactions to vastly improve customer engagement and loyalty.
Improved business productivity
It’s estimated that knowledge workers spend 20% of their time searching for and collecting information. With generative AI, they can instantly review mountains of data in plain human language, unlocking faster insights.
Better compliance and risk management
Generative AI can continually monitor large amounts of data and quickly identify potential compliance or risk issues before they occur. That helps organizations prevent fraud and detect other anomalies to better protect their customers and themselves.
Accurate predictive analytics
Businesses can build predictive generative AI models to improve everything from retail forecasting and product pipelines to transportation scheduling and health treatments. For example, advanced machine learning (ML) capabilities can analyze large volumes of medical data to help healthcare professionals identify patients at high risk for heart failure or other diseases, allowing for earlier intervention and better outcomes.
Enhanced cybersecurity
Generative AI can help organizations solidify their defenses against a range of cybersecurity risks. It can detect network anomalies that may indicate security threats. Generative models can analyze network files and applications to detect malware and be trained to recognize evolving malicious software threats. They can also bolster privacy protections and compliance by anonymizing sensitive data.
Lower technical debt
Technical debt can account for up to 40% of IT budgets, partly made up by time-consuming manual development processes. Generative AI can eliminate some of that tech debt by accelerating code development and refactoring and automating testing. In fact, research shows generative AI can accelerate code development time by up to 45% and help engineers refactor code up to 30% more quickly.
Programming languages for generative AI
Developers have a few options when it comes to selecting a programming language to use for building generative systems. They include Python, R, and Julia. Of these, the most used is Python, as it features a combination of:
- A comprehensive library. With Python, developers can access pre-built.
APIs and modules for a wide range of generative AI techniques and take advantage of rich libraries and frameworks focused on machine and deep learning, including PyTorch, TensorFlow, and Keras. That accelerates development time and facilitates model experimentation.
- Easy-to-understand syntax. Python offers a simple, readable syntax that makes it easier to work with for a larger audience of users besides developers, including data scientists and researchers. Its simplicity allows for more efficient development and model experimentation.
- Technology integrations. Generative AI relies on several technologies to go from development to production, including databases, cloud platforms, frameworks, and more. Python easily integrates with these systems to make for smoother generative AI deployments.
- Rich community support knowledge-base. A large and active Python user base helps developers tap into a large network of valuable resources and knowledge. That can speed up problem-solving, accelerate development and testing, and educate new users.
FAQ
What are the most important things to consider when selecting an AI tech stack?
AI tech stacks are not one-size-fits-all. So, when selecting the right AI tech stack for your organization, you’ll want to ensure that it aligns with the specific goals of your initiatives. That includes considering what types of data text you’ll be processing, such as text, images, or audio, as well as how well the AI tech stack integrates with existing databases, systems, and other tools already in place.
You’ll also want to make sure your AI tech stack is scalable, cost-effective, and easy to upgrade as your needs evolve. Plus, the AI tech stack should be adept at handling data privacy and security, and it should comply with applicable industry privacy regulations.
Should my organization build a team dedicated to generative AI technology?
Deciding on whether to build a dedicated team for generative AI depends largely on your organization's goals, the complexity and scale of your AI initiatives, and the resources available. For example, if AI is expected to be a major business driver and part of the long-term core strategy for your enterprise, then a dedicated team may make sense. Alternatively, you could choose to opt for a combination of dedicated internal team members and external partnerships to save on costs and make up for any skills gaps.
What are the challenges of building an AI tech stack?
Some of the biggest hurdles to building an effective AI tech stack include:
- Integrations between new technologies and legacy systems
- Implementation and management of the right infrastructure and necessary hardware or cloud resources
- Data privacy and security, cost management, and scalability so the tech is flexible to grow as your data needs do
Working with an experienced cloud analytics and data platform provider can help you create an AI tech stack that aligns with your organization's strategy and goals.
Teradata VantageCloud can deliver AI tech stack deployment options that work best for your enterprise’s needs and goals, from cloud to hybrid and everything in between. With transparent pricing, trusted AI tools, advanced analytics, and flexible scalability options, VantageCloud allows you to adapt your stack as needed while reducing costs and complexity.
