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From Pilot to Production: How to Scale Generative AI

Generative AI is revolutionizing industries, powering applications from customer service chatbots to innovative content creation tools. But while piloting these projects can be relatively straight forward, scaling them into production-ready solutions that meet enterprise demands presents significant challenges.

Generative AI has emerged as a transformative force, enabling applications that range from customer service chatbots to content generation tools. However, many organizations face a critical challenge: transitioning from initial experimentation to production at scale. While piloting a generative AI project is an exciting step, scaling to a production-ready solution that meets enterprise requirements demands more robust infrastructure, optimized tools, and secure deployment practices.

NVIDIA, in partnership with Microsoft Azure, offers a powerful suite of solutions to help businesses overcome these challenges. Their collaboration, highlighted during the Microsoft Ignite 2024 session “From Pilot to Production: The Quick, Easy, and Optimized Way,” showcases best practices and tools for scaling generative AI projects seamlessly.

In this blog, we’ll explore how organizations can overcome common barriers, unlock the full potential of generative AI, and deploy scalable, secure, and high-performing AI applications. Let’s dive into actionable strategies, real-world examples, and insights to accelerate your journey.

The Challenge of Scaling Generative AI

Scaling generative AI is not as simple as expanding a pilot. Generative AI adoption often begins with pilots, leveraging hosted API endpoints like Open AI’s services for quick experimentation. While effective for early-stage testing, this approach can fall short when enterprises need:

  • Data Privacy and Security: Many hosted endpoints require sending proprietary data over networks, raising security concerns.
  • Customization: General-purpose models may not perform well on domain-specific or proprietary data.
  • Control: Hosted solutions limit the ability to fine-tune models and optimize infrastructure.

To bridge this gap, enterprises must shift to scalable, secure solutions that enable greater control over their AI applications.

Scalable Solutions

Scaling generative AI from pilot to production requires more than just powerful models—it demands a robust, flexible platform that simplifies development and deployment.

The partnership between NVIDIA and Microsoft Azure offers a robust foundation for scaling generative AI, combining GPU-accelerated infrastructure with enterprise-grade tools to simplify and optimize generative AI deployments. By leveraging Azure Foundry alongside NVIDIA’s AI Enterprise tools, organizations can accelerate their journey from experimentation to enterprise-grade applications.

NVIDIA AI Enterprise

NVIDIA’s AI Enterprise platform is designed to address these challenges, providing a full-stack AI solution that integrates seamlessly with Microsoft Azure. Key features include:

  • NVIDIA Inference Microservice (NIM): Prebuilt, containerized runtimes for deploying generative AI models with minimal latency and high throughput.
  • Custom  Model Support: NVIDIA NeMo enables fine-tuning models with proprietary data to enhance relevance and accuracy.
  • Flexibility Across Environments: Deployable across Azure VMs, AI Foundry, and even Edge environments, NIM offers unmatched portability.

Optimizing Performance with NIM

One of NVIDIA’s standout innovations, NIM, simplifies the deployment of large language models (LLMs) and other generative AI applications. Its key benefits include:

  1. Ease of Deployment: With just a few lines of code, NIM containers are ready to run, enabling developers to focus on building applications instead of managing infrastructure.
  2. Performance Optimization: By leveraging GPU-specific optimizations, NIM delivers up to 2.5x faster token generation compared to open-source stacks.
  3. Support for Multiple Models: From domain-specific models to LLMs like Llama and Mistral, NIM provides pre-optimized runtimes tailored to specific workloads.

Azure Foundry

Azure Foundry, part of Microsoft’s cloud ecosystem, plays a critical role in enabling enterprises to operationalize AI efficiently and securely. Azure Foundry is a cloud-based development platform designed to streamline the creation, deployment, and management of AI and machine learning (ML) workflows. With features tailored for scalability, security, and integration, it provides a centralized environment where teams can:

  • Develop AI applications collaboratively.
  • Deploy containerized workloads across various environments.
  • Monitor and optimize AI performance with built-in analytics.

Why Azure Foundry is Ideal for Generative AI

When paired with NVIDIA’s AI Enterprise solutions, Azure Foundry becomes a powerhouse for scaling generative AI. Key benefits include:

1. Streamlined Development and Deployment
  • Azure Foundry supports containerized AI workflows, such as NVIDIA’s Inference Microservices (NIM). This compatibility enables seamless deployment of large language models (LLMs) or domain-specific AI models, reducing the operational overhead for developers.
  • Prebuilt integration with Azure Machine Learning further accelerates experimentation, training, and deployment pipelines.
2. Scalability and Performance
  • Azure Foundry leverages Azure GPU-accelerated virtual machines, ensuring high throughput and low latency for real-time generative AI applications.
  • As workloads grow, Foundry’s scalable infrastructure ensures consistent performance, whether you’re deploying on the cloud or extending to edge environments.
3. Data Security and Compliance
  • Generative AI solutions often require handling sensitive or proprietary data. Azure Foundry offers virtual private cloud (VPC) options and compliance certifications for industries like healthcare and finance, enabling secure deployments that meet stringent regulatory requirements.
4. Flexibility Across Deployment Environments
  • With Azure Foundry, enterprises can deploy AI applications across various environments, including:
    • On-premises for maximum control over sensitive workloads.
    • Azure cloud for scalability and efficiency.
    • Hybrid or edge setups for latency-sensitive applications like IoT or customer-facing chatbots.

A Real-World Example: Pizza-Ordering Bot

During Microsoft Ignite, NVIDIA demonstrated the capabilities of its technology with a pizza-ordering bot. The pizza-ordering bot, built using three NIMs, demonstrated how to create a low-latency, fully interactive application by utilizing:

  • Speech-to-Text Conversion: Capturing customer orders with natural language processing.
  • LLM for Conversation: Using Llama 3.1 to guide interactions.
  • Text-to-Speech: Providing real-time responses to users.

By leveraging NVIDIA's optimized infrastructure on Azure, the bot handled real-time user input, processed it through a large language model, and delivered smooth, human-like responses—showcasing the potential for enterprise applications in customer service and beyond. The demo illustrated how enterprises can create interactive, production-ready AI applications that deliver seamless user experiences.

Best Practices for Generative AI Success

For organizations looking to move from pilot to production, consider these key steps:

  1. Choose the Right Infrastructure: Leverage GPU-accelerated environments like Azure VMs and AI Foundry to ensure performance and scalability.
  2. Prioritize  Data Privacy: Opt for on-premises or virtual private cloud deployments when working with sensitive proprietary data.
  3. Optimize for Performance: Use tools like NIM to reduce latency and improve throughput without sacrificing model accuracy.
  4. Iterate and Improve: Continuously fine-tune models using feedback loops, proprietary data, and emerging technologies to stay competitive

The Future of Generative AI is Here

Generative AI offers unparalleled opportunities to transform industries—but scaling these solutions requires the right tools, infrastructure, and expertise. With NVIDIA and Microsoft Azure, your organization can move from experimentation to production faster, safer, and more effectively.

Whether you’re piloting your first generative AI project or looking to optimize an existing application, the resources and insights available through platforms like NVIDIA Launch Pad and Azure’s AI services can guide you on the journey from experimentation to production.

Explore more at NVIDIA’s Build Platform or try a hands-on lab to see how these tools can elevate your AI applications.  For assistance optimizing these tools for your organization’s specific goals, reach out to one of our Navigators today!

Don’t let challenges hold you back — unlock the future of AI today.

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Generative AI is revolutionizing industries, powering applications from customer service chatbots to innovative content creation tools. But while piloting these projects can be relatively straight forward, scaling them into production-ready solutions that meet enterprise demands presents significant challenges.

Generative AI has emerged as a transformative force, enabling applications that range from customer service chatbots to content generation tools. However, many organizations face a critical challenge: transitioning from initial experimentation to production at scale. While piloting a generative AI project is an exciting step, scaling to a production-ready solution that meets enterprise requirements demands more robust infrastructure, optimized tools, and secure deployment practices.

NVIDIA, in partnership with Microsoft Azure, offers a powerful suite of solutions to help businesses overcome these challenges. Their collaboration, highlighted during the Microsoft Ignite 2024 session “From Pilot to Production: The Quick, Easy, and Optimized Way,” showcases best practices and tools for scaling generative AI projects seamlessly.

In this blog, we’ll explore how organizations can overcome common barriers, unlock the full potential of generative AI, and deploy scalable, secure, and high-performing AI applications. Let’s dive into actionable strategies, real-world examples, and insights to accelerate your journey.

The Challenge of Scaling Generative AI

Scaling generative AI is not as simple as expanding a pilot. Generative AI adoption often begins with pilots, leveraging hosted API endpoints like Open AI’s services for quick experimentation. While effective for early-stage testing, this approach can fall short when enterprises need:

  • Data Privacy and Security: Many hosted endpoints require sending proprietary data over networks, raising security concerns.
  • Customization: General-purpose models may not perform well on domain-specific or proprietary data.
  • Control: Hosted solutions limit the ability to fine-tune models and optimize infrastructure.

To bridge this gap, enterprises must shift to scalable, secure solutions that enable greater control over their AI applications.

Scalable Solutions

Scaling generative AI from pilot to production requires more than just powerful models—it demands a robust, flexible platform that simplifies development and deployment.

The partnership between NVIDIA and Microsoft Azure offers a robust foundation for scaling generative AI, combining GPU-accelerated infrastructure with enterprise-grade tools to simplify and optimize generative AI deployments. By leveraging Azure Foundry alongside NVIDIA’s AI Enterprise tools, organizations can accelerate their journey from experimentation to enterprise-grade applications.

NVIDIA AI Enterprise

NVIDIA’s AI Enterprise platform is designed to address these challenges, providing a full-stack AI solution that integrates seamlessly with Microsoft Azure. Key features include:

  • NVIDIA Inference Microservice (NIM): Prebuilt, containerized runtimes for deploying generative AI models with minimal latency and high throughput.
  • Custom  Model Support: NVIDIA NeMo enables fine-tuning models with proprietary data to enhance relevance and accuracy.
  • Flexibility Across Environments: Deployable across Azure VMs, AI Foundry, and even Edge environments, NIM offers unmatched portability.

Optimizing Performance with NIM

One of NVIDIA’s standout innovations, NIM, simplifies the deployment of large language models (LLMs) and other generative AI applications. Its key benefits include:

  1. Ease of Deployment: With just a few lines of code, NIM containers are ready to run, enabling developers to focus on building applications instead of managing infrastructure.
  2. Performance Optimization: By leveraging GPU-specific optimizations, NIM delivers up to 2.5x faster token generation compared to open-source stacks.
  3. Support for Multiple Models: From domain-specific models to LLMs like Llama and Mistral, NIM provides pre-optimized runtimes tailored to specific workloads.

Azure Foundry

Azure Foundry, part of Microsoft’s cloud ecosystem, plays a critical role in enabling enterprises to operationalize AI efficiently and securely. Azure Foundry is a cloud-based development platform designed to streamline the creation, deployment, and management of AI and machine learning (ML) workflows. With features tailored for scalability, security, and integration, it provides a centralized environment where teams can:

  • Develop AI applications collaboratively.
  • Deploy containerized workloads across various environments.
  • Monitor and optimize AI performance with built-in analytics.

Why Azure Foundry is Ideal for Generative AI

When paired with NVIDIA’s AI Enterprise solutions, Azure Foundry becomes a powerhouse for scaling generative AI. Key benefits include:

1. Streamlined Development and Deployment
  • Azure Foundry supports containerized AI workflows, such as NVIDIA’s Inference Microservices (NIM). This compatibility enables seamless deployment of large language models (LLMs) or domain-specific AI models, reducing the operational overhead for developers.
  • Prebuilt integration with Azure Machine Learning further accelerates experimentation, training, and deployment pipelines.
2. Scalability and Performance
  • Azure Foundry leverages Azure GPU-accelerated virtual machines, ensuring high throughput and low latency for real-time generative AI applications.
  • As workloads grow, Foundry’s scalable infrastructure ensures consistent performance, whether you’re deploying on the cloud or extending to edge environments.
3. Data Security and Compliance
  • Generative AI solutions often require handling sensitive or proprietary data. Azure Foundry offers virtual private cloud (VPC) options and compliance certifications for industries like healthcare and finance, enabling secure deployments that meet stringent regulatory requirements.
4. Flexibility Across Deployment Environments
  • With Azure Foundry, enterprises can deploy AI applications across various environments, including:
    • On-premises for maximum control over sensitive workloads.
    • Azure cloud for scalability and efficiency.
    • Hybrid or edge setups for latency-sensitive applications like IoT or customer-facing chatbots.

A Real-World Example: Pizza-Ordering Bot

During Microsoft Ignite, NVIDIA demonstrated the capabilities of its technology with a pizza-ordering bot. The pizza-ordering bot, built using three NIMs, demonstrated how to create a low-latency, fully interactive application by utilizing:

  • Speech-to-Text Conversion: Capturing customer orders with natural language processing.
  • LLM for Conversation: Using Llama 3.1 to guide interactions.
  • Text-to-Speech: Providing real-time responses to users.

By leveraging NVIDIA's optimized infrastructure on Azure, the bot handled real-time user input, processed it through a large language model, and delivered smooth, human-like responses—showcasing the potential for enterprise applications in customer service and beyond. The demo illustrated how enterprises can create interactive, production-ready AI applications that deliver seamless user experiences.

Best Practices for Generative AI Success

For organizations looking to move from pilot to production, consider these key steps:

  1. Choose the Right Infrastructure: Leverage GPU-accelerated environments like Azure VMs and AI Foundry to ensure performance and scalability.
  2. Prioritize  Data Privacy: Opt for on-premises or virtual private cloud deployments when working with sensitive proprietary data.
  3. Optimize for Performance: Use tools like NIM to reduce latency and improve throughput without sacrificing model accuracy.
  4. Iterate and Improve: Continuously fine-tune models using feedback loops, proprietary data, and emerging technologies to stay competitive

The Future of Generative AI is Here

Generative AI offers unparalleled opportunities to transform industries—but scaling these solutions requires the right tools, infrastructure, and expertise. With NVIDIA and Microsoft Azure, your organization can move from experimentation to production faster, safer, and more effectively.

Whether you’re piloting your first generative AI project or looking to optimize an existing application, the resources and insights available through platforms like NVIDIA Launch Pad and Azure’s AI services can guide you on the journey from experimentation to production.

Explore more at NVIDIA’s Build Platform or try a hands-on lab to see how these tools can elevate your AI applications.  For assistance optimizing these tools for your organization’s specific goals, reach out to one of our Navigators today!

Don’t let challenges hold you back — unlock the future of AI today.

Back to top

More from
Latest news

Discover latest posts from the NSIDE team.

Recent posts
About
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