Unlocking AI Potential: Maximize Pure Storage and NVIDIA Capabilities

Enterprises know that AI is a game changer, but without the right tools and strategy in place, success is not guaranteed. This expert-led webinar provides a roadmap to unlocking the power of AI using the advanced capabilities of Pure Storage and NVIDIA. You’ll learn how to use pretrained models to optimize performance, perform fine-tuning techniques to achieve better results, and prepare your data and teams for success.

Understanding AI: Do, Adapt, and Use

When it comes to AI, there are three main ways to think about how organizations use it. It's not a one-size-fits-all situation, and each approach has its own needs and challenges.

Do AI: Building From Scratch

This is for organizations that are creating brand-new foundational AI models. Think of companies like OpenAI, Microsoft, Google, or Anthropic. They're doing the deep research and development. To do this, you need:

• Massive Amounts of Data: Billions, even trillions, of data points are common. This data also needs to be super clean and well-managed.

• Lots of Compute Power: We're talking about powerful GPUs, CPUs, and fast connections between them.

• Top-Tier Talent: You'll need world-class researchers, data scientists, data architects, and data engineers.

This approach is usually for organizations with a huge amount of curated data or those specifically focused on AI development.

Use AI: Leveraging Existing Capabilities

This is where AI is already built into a product or service. Sometimes it's obvious, like using ChatGPT, where you directly interact with the AI. Other times, it's more hidden, like co-pilot features in Microsoft products (Teams, Excel, Word, PowerPoint).

These tools are changing how we interact with computers. Instead of clicking around, we're using natural language to get things done. For example, asking co-pilot to summarize an email. The challenge here is that these capabilities are evolving so fast, so users need to be retrained constantly. There's a lot of organizational change management involved to help people adopt these new ways of working.

Adapt AI: Customizing for Your Business

This is the middle ground, and it's where many businesses will find themselves. You take a foundational model built by someone else (like Google or Meta) and customize it for your specific business needs. This involves:

• Curated Data Sets: You'll still need good, managed data, but not on the same scale as the "Do AI" category.

• Skilled Personnel: You'll need data scientists, architects, and engineers, but perhaps fewer than if you were building models from scratch.

This approach lets you take powerful existing AI and make it work with your unique data, then present it to your internal or external users.

Key Takeaways

• Fine-Tuning: This means taking a foundational model (like Gemini or Llama 2) and retraining it with your specific data for a very specialized task. For example, fine-tuning a general model for medical or retail use.

• Embedding: This is about giving a foundational model access to your data sets so it can search through them as part of its question-and-answer process. If you ask for marketing email text, it can also tell you where that text came from within your data.

The Role of NVIDIA and Pure Storage in AI

Two key players in the AI space are NVIDIA and Pure Storage, and they work together to provide a strong foundation for AI development and deployment.

NVIDIA AI Enterprise: A Comprehensive Framework

NVIDIA AI Enterprise is an end-to-end platform that helps manage AI workflows across all three models (Do, Adapt, Use). It gives users the tools to interact with AI in whatever way they need. This framework provides access to many pre-trained models, as well as the ability to fine-tune and embed data. It also supports all the data science and development needed to get the desired outcomes.

One important aspect is its cloud-native operation. This means it works consistently whether you're using an all-cloud environment, a private infrastructure, or a hybrid model. It also helps optimize the use of expensive resources, which is a big plus.

This framework helps accelerate AI compute regardless of location—cloud, data center, or even at the edge. For example, if you've trained and fine-tuned a model, you can use it for inferencing at the edge, like in a mobile device or a smart robot.

Pure Storage: High-Performance Data Delivery

When you're in the "Adapt AI" model, you need a lot of information, and it needs to be delivered quickly. Pure Storage provides the performance needed to feed data to those hungry GPUs and CPUs that are doing all the training and fine-tuning. These processors can consume massive amounts of data very, very fast.

Pure Storage connects seamlessly with the NVIDIA infrastructure, creating a complete framework. This combination provides a solid foundation for organizations to build and deploy their AI solutions. It's about making sure the data is there, ready and waiting, when the AI needs it, at the speed it needs it.

Unlocking the full potential of AI requires a clear strategy and the right tools. By understanding the different ways to approach AI—whether building from scratch, using existing solutions, or adapting models to your specific needs—organizations can make informed decisions. The combination of NVIDIA's comprehensive AI framework and Pure Storage's high-performance data solutions provides a powerful foundation for any business looking to leverage AI effectively.