Imagine this. You’re finally ready to deploy your generative AI model. But the moment you do, it suddenly crashes and burns, leaving you to pick up the pieces. This is the last thing you want to happen after months of research and development. What went wrong? More often than not, it’s not the model itself at fault but the infrastructure it was built on. This is why creating a reliable and scalable generative AI infrastructure is crucial to ensure your AI models' efficient deployment and performance in production environments. In this article, we’ll unpack the significance of generative AI infrastructure, common challenges that arise from inadequate infrastructure, and how you can build a robust solution to ensure smooth sailing for your project.
Lamatic’s generative AI tech stack is a valuable tool for achieving objectives such as building a reliable and scalable generative AI infrastructure that can seamlessly handle high computational demands, scale effortlessly with growing data and user needs, and ensure efficient deployment and performance of AI models in production environments.
What is Generative AI Infrastructure and Its Importance
Generative AI infrastructure is the hardware, software, and networking resources required to:
- Develop
- Deploy
- Manage generative AI models
Key components of this infrastructure include:
- GPUs
- Cloud services
- Specialized frameworks
For example, popular frameworks for generative AI include:
- TensorFlow
- PyTorch
Due to their high computational demands, generative AI models would only be practical with this infrastructure. This infrastructure ensures efficient:
- Training
- Scalability
- Real-time performance
This is critical for AI applications across industries. Generative AI infrastructure providers focus on researching and developing the foundational AI techniques, while application developers build products using those foundational technologies.
AI Infrastructure vs IT Infrastructure: What’s the Difference?
Generative AI infrastructure is a subset of AI infrastructure distinct from IT infrastructure. AI infrastructure, an AI stack, refers to the hardware and software needed to create and deploy AI-powered applications and solutions.
Robust AI infrastructure enables developers to effectively create and deploy AI and machine learning (ML) applications like chatbots such as:
- OpenAI’s Chat GPT
- Facial and speech recognition
- Computer vision
Enterprises of all sizes and across various industries depend on AI infrastructure to help them realize their AI ambitions. As enterprises discover more ways to use AI, creating the infrastructure required to support its development has become paramount.
Infrastructure Demands for AI Projects
Whether deploying ML to spur innovation in the supply chain or preparing to release a generative AI chatbot, having the proper infrastructure is crucial. AI projects require bespoke infrastructure primarily because of the power needed to run AI workloads.
AI infrastructure depends on cloud environments' low latency and the processing power of graphics processing units (GPUs) rather than the more traditional central processing units (CPUs) typical of conventional IT infrastructure environments to achieve this kind of power.
AI infrastructure concentrates on hardware and software optimized for cloud-based AI and ML tasks, rather than traditional IT infrastructure, which typically emphasizes:
- PCs
- Software
- On-premise data centers
In an AI ecosystem, software stacks typically include:
- ML Libraries and Frameworks: TensorFlow, PyTorch
- Programming Languages: Python, Java
- Distributed Computing Platforms: Apache Spark, Hadoop
Generative AI Infrastructure Providers: The New AI Ecosystem
Generative AI (GenAI) infrastructure providers are vendors, including cloud platforms and hardware manufacturers, that offer:
- Underlying technology
- Tools
- Hardware
These resources enable companies and developers to build and deploy generative AI applications in production environments. Generative AI refers to technologies capable of creating:
- New, derived versions of content
- Strategies
- Designs
- Methods
These providers offer scalable, reliable, and cost-effective solutions for generative AI projects, which can be complex and expensive to train and deploy.
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How To Build a Reliable and Scalable Generative AI Infrastructure
Deciding on the Right Foundation Model for Your Generative AI Infrastructure
With countless generative AI models available, picking one that aligns with your organization’s goals is critical. As organizations explore the world of foundation models, they’ll find options from several sources, including:
- Proprietary
- Open-source models
Leading providers offer next-generation models as a service, developed through fundamental research and trained on a large corpus of publicly available data. Cloud hyperscalers are also getting into the game by:
- Partnering with the pure-plays
- Adopting open-source models
- Pre-training their models
- Providing full-stack services
It’s worth noting that smaller, lower-cost foundation models (such as Databricks’ Dolly) are making building or customizing generative AI increasingly accessible. All options must be carefully considered to fit your organization’s needs and requests.
Making Generative AI Infrastructure Accessible for Your Organization
Businesses can take two principal approaches to accessing generative AI models:
- Full control
- Managed cloud service
On-Premise Deployment: Pros and Cons
The first option lets organizations deploy models on their public cloud (e.g., cloud hyperscalers) or private infrastructure (e.g., private cloud, data centers). This approach requires identifying and managing the proper infrastructure for these models and developing associated talent and skills. It also entails controlling the models and developing full-stack services for easier adoption.
Alternatively, organizations can opt for speed and simplicity by accessing generative AI as a managed cloud service from an external vendor. Both options have their merits, but if you choose complete control, you must know several additional factors.
Adapting Foundation Models to Your Own Data
Getting maximum business value from generative AI often depends on leveraging your proprietary data to boost:
- Accuracy
- Performance
- Usefulness within the enterprise
Several ways exist to adapt pre-trained models to your data for use within the organization. You can buy an utterly pre-trained model “off the shelf” and use in-context learning techniques to get responses with your data.
Data Foundation for Accelerated AI Value
You can also boost a mainly pre-trained model by adding your data on top through fine-tuning. Of course, you can build your model ground-up (or pre-train further from open-sourced ones) on your infrastructure using your data. To do this at speed and scale, you first need a modern data foundation that makes consuming data through the foundation models easier. This is a prerequisite for extracting accelerated and exponential value with generative AI.
Assessing Your Organization’s Overall Readiness for Generative AI
It’s critical to ensure that foundation models meet the following enterprise requirements:
- Overall security
- Reliability
- Responsibility
Integration and interoperability frameworks are also crucial for enabling full-stack solutions with foundation models in the enterprise. Nevertheless, for AI to be enterprise-ready, organizations must trust it, which raises all sorts of considerations.
Mitigating AI Risks for Sensitive Business Functions
Companies must consider the AI implications of adopting this technology for sensitive business functions. Built-in capabilities from generative AI vendors are maturing, but you must develop your controls and mitigation techniques as appropriate.
Proactive AI Governance for Enterprise Security
Companies can take several practical actions to ensure generative AI doesn’t threaten enterprise security. Adopting generative AI is an ideal time to review your overall AI governance standards and operating models.
Considering the Environmental Impact of Generative AI
Although they come pre-trained, foundation models can still require significant energy during adaptation and fine-tuning. This becomes very significant if you consider pre-training your model or building it from the ground up.
Environmental Impact of Foundation Model Adoption
Different implications depend on the approach to:
- Buying
- Boosting
- Creating the foundation models
Left unchecked, scaling up applications based on generative AI across the enterprise will significantly impact the organization’s carbon footprint. So, the potential environmental impact needs to be considered upfront in making the right choices about the available options.
Industrializing Generative AI App Development
After choosing and deploying a foundation model, companies must consider what new frameworks may be required to industrialize and accelerate application development. Vector databases or domain knowledge graphs that capture business data and broader knowledge (such as how business concepts are structured) also become essential for developing valuable applications with generative AI.
Industrializing Prompt Engineering for Competitive Advantage
Prompt engineering techniques are fast becoming a differentiating capability. By industrializing the process, you can build a corpus of efficient, well-designed prompts and templates aligned to specific business functions or domains. Look to incorporate enterprise frameworks to scale collaboration and management around them.
An orchestration framework is key for application enablement, as stitching together a generative AI application involves coordinating multiple components, services, and steps.
Understanding What It Takes to Operate Generative AI at Scale
Consider their impact on operability as your generative AI applications launch and run. Some companies have already developed an MLOps framework to productize ML applications.
Those standards require a thorough review to incorporate LLMOps and Gen AIOps considerations and accommodate changes in DevOps, CI/CD/CT, model management, model monitoring, prompt management, and data/knowledge management in pre-production and production environments.
The MLOps approach will have to evolve for the world of foundation models, considering processes across the whole application lifecycle. As generative AI leads to AutoGPT—where AI powers much more of the end-to-end process—we’ll witness AI driving an operations architecture that automates:
- Productionizing
- Monitoring
- Calibrating
These models and their interactions ensure the continued delivery of business SLAs.
Lamatic: Your Managed GenAI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low-Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge Deployment via Cloudflare Workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
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Why Is a Comprehensive Tech Stack Essential in Building Effective Generative AI Systems?
Machine Learning Frameworks: The Backbone of Generative AI
Generative AI systems rely on complex machine learning models to create new data. Machine learning frameworks provide the functionality to build and train models, including:
- TensorFlow
- Keras
- PyTorch
These frameworks offer APIs and tools for different tasks and support a variety of pre-built models for:
- Image
- Text
- Music generation
This flexibility allows users to design and customize models to achieve the desired level of accuracy and quality. These frameworks should be integral to the generative AI tech stack.
Programming Languages: Building the Generative AI System
Programming languages are crucial in building generative AI systems that balance ease of use and the performance of generative AI models.
Python is the most commonly used language in machine learning and is preferred for building generative AI systems due to its:
- Simplicity
- Readability
- Extensive library support
Other programming languages, like R and Julia, are also sometimes used.
Cloud Infrastructure: Powering Generative AI Applications
Generative AI systems require large amounts of computing power and storage capacity to train and run the models. Including cloud infrastructures in a generative AI tech stack is essential, providing the scalability and flexibility needed to deploy generative AI systems.
Cloud providers offer services, including virtual machines, storage, and machine learning platforms, such as:
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
Data Processing Tools: Making Data Ready for Generative AI
Data is critical in building generative AI systems. The data must be preprocessed, cleaned, and transformed before it can be used to train the models. Data processing tools commonly used in a generative AI tech stack for efficiently handling large datasets include:
- Apache Spark
- Apache Hadoop
These tools also provide data visualization and exploration capabilities, which can help understand the data and identify patterns.
Get Started With Generative AI Today
A well-designed generative AI tech stack can improve the system's:
- Accuracy
- Scalability
- Reliability
This enables faster development and deployment of generative AI applications.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack. Their solution provides managed GenAI middleware, custom GenAI API (GraphQL), low-code agent builder, automated GenAI workflow (CI/CD), GenOps (DevOps for GenAI), edge deployment via Cloudflare workers, and integrated vector database (Weaviate).
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Their platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with Lamatic’s managed generative AI tech stack.
