Unlocking the Power of AI Infrastructure

Table of Contents

1. Introduction
2. The State of the AI Landscape
3. Getting Started with an AI Project
4. The Role of Infrastructure in AI Development
5. The Challenges of Shared Infrastructure
6. Introducing FlashBlade: Consolidating AI Workloads
7. Other Workloads Supported by FlashBlade
8. Core Scientific: Cloud for Data Scientists
9. The Benefits of AI as a Service
10. Getting Started with Core Scientific AI-as-a-Service

The State of AI Infrastructure and the Future of Data Science

Artificial intelligence (AI) has revolutionized the way businesses operate in every industry. From healthcare to logistics to customer service, AI is being used to streamline operations, improve outcomes, and drive innovation. However, harnessing the power of AI requires not only advanced algorithms and models but also a robust infrastructure to support the complex computational requirements.

In this article, we will explore the Current state of AI infrastructure, the challenges faced by organizations in deploying AI projects, and the future of data science. We will discuss the importance of infrastructure in AI development and how organizations can overcome the hurdles of shared infrastructure. Additionally, we will introduce FlashBlade, a leading solution for consolidating AI workloads, and explore the benefits of AI as a service with Core Scientific.

The State of the AI Landscape

The AI landscape is expanding rapidly, with businesses across industries realizing the potential of AI for transforming their operations. From improving clinical outcomes to enhancing customer interactions, AI is being used to tackle valuable low-hanging fruit in every sector. Enterprises are finding tenable use cases that can be deployed now to initiate their business transformation.

The reach of attractive AI use cases extends to every industry, fueled by the data organizations already possess. The challenge now is to effectively tap into the power of AI and harness the full potential of the data. This requires training complex models with exponentially increasing data sets and ensuring rapid iteration in the development cycle. Supercomputing Scale is essential for accomplishing this, making every enterprise a supercomputing enterprise if they want to thrive in the AI era.

To support the development of AI applications, organizations need to bring together a diverse team of experts. Data scientists, application developers, data engineers, and IT management all play critical roles in enabling the development and deployment of AI solutions. It is essential to establish the right infrastructure to support data science practitioners and ensure the scalability and performance of AI models.

Getting Started with an AI Project

Starting an AI project requires a multidisciplinary team and a clear understanding of the problem You are trying to solve. Begin by identifying a fundamental problem or challenge that your organization is facing, which can be addressed by AI. This problem should have a quantifiable impact on your business, whether it's lowering costs, improving productivity, or enhancing customer experiences.

An executive sponsor who has a vested interest in solving the problem is also crucial for securing buy-in and support throughout the organization. Define a modest initial use case that aligns with the business problem and focus on achieving repeatability and follow-on use cases. Starting small and gradually expanding allows the team to develop expertise and build Momentum for future AI initiatives.

Data plays a vital role in AI projects, and it is essential to review and understand the data available within your organization. Identify where the data resides, how it is accessed, and any transformations or labeling required for training AI models. Collaborate closely with application developers to build the necessary infrastructure and deploy AI software stacks that allow data scientists to focus on creative experimentation and model development.

The Role of Infrastructure in AI Development

Infrastructure plays a critical role in AI development and training models. To achieve the full potential of AI, organizations need to provide data scientists with the fastest path to productive experimentation and model development. They should be able to scale effortlessly, iterate on models, and optimize them for specific use cases without worrying about the underlying platform.

Data engineering and ML Ops teams are responsible for delivering scalable data pipelines to support model development and training, as well as production inference. They play a critical role in industrializing or mechanizing AI in a production setting. IT management teams need to enable all of this by providing a proven standard for AI infrastructure that eliminates design complexity, delivers predictable performance, and is easy to manage with familiar tools.

However, many organizations are running AI projects in the absence of IT involvement. This lack of infrastructure strategy prevents them from unlocking the full potential of AI and achieving the desired successes. The availability of convenient cloud computing options has contributed to the proliferation of shadow AI projects and the complexity of managing infrastructure.

The Challenges of Shared Infrastructure

Shared infrastructure has become a significant challenge for organizations deploying AI projects. These projects often rely on separate pockets of shadow AI, where individual developer teams procure their own resources and run their own projects. This leads to a sprawl of independent projects, each addressing a different business problem and using different infrastructure.

While cloud computing offers accessibility and flexibility, it can become cost-prohibitive at scale. Additionally, public cloud providers may not offer the latest and greatest GPUs, causing organizations to fall behind in terms of hardware capabilities. The lack of GPU saturation and the complexity of managing a shared infrastructure further compound the challenges of using shared resources for AI workloads.

To overcome these challenges, organizations need to proactively Create an AI Center of Excellence. This shared infrastructure approach consolidates AI expertise, standardizes best practices, and accelerates time-to-solution for AI initiatives. By building their private machine learning cloud, organizations can maximize resource utilization and provide capacity to all developers across lines of business while driving down costs.

Introducing FlashBlade: Consolidating AI Workloads

FlashBlade, offered by Pure Storage, is a leading solution for consolidating AI workloads. It addresses the challenges of shared infrastructure by providing a unified platform that supports multiple analytics and AI workloads. With FlashBlade, organizations can consolidate their workloads and benefit from Simplified design, faster deployment, effortless scale, and manageability.

In addition to AI workloads, FlashBlade supports modern analytics, including cloud analytics and data lakes. It also excels in rapid restore use cases, providing fast data recovery compared to traditional backup devices. The scalability and performance of FlashBlade's unified platform enable organizations to optimize for both price and performance while running multiple workloads simultaneously.

Other Workloads Supported by FlashBlade

FlashBlade's versatility extends beyond AI workloads. It supports various data analytics and processing GPU accelerators, making it suitable for modern analytics use cases. Whether it's data warehousing, streaming analytics, or elastic computing, FlashBlade provides uncompromised performance for diverse workloads.

With FlashBlade, organizations can consolidate their siloed data sets and workloads, eliminating the complexity of managing multiple data stores. This simplifies infrastructure and enables seamless data sharing across applications while ensuring high-performance outcomes.

Core Scientific: Cloud for Data Scientists

Core Scientific offers AI as a service, bringing the capabilities of the public cloud to on-premise and colocation deployments. This approach empowers data scientists to leverage cutting-edge AI infrastructure while keeping their data close to their existing data lakes. It provides the ease of public cloud tooling and workflows without sacrificing data security and privacy.

Core Scientific's solution, Plexus, acts as a miniature version of a public cloud software stack. It offers a web-Based access portal, marketplace integration, and access to the latest AI infrastructure. Data scientists can launch notebooks, run scripts, and leverage common data sets on their infrastructure, enhancing collaboration and productivity.

The Benefits of AI as a Service

AI as a service offers several benefits for organizations looking to accelerate their AI initiatives. By bringing the cloud to your data, AI as a service eliminates the need for data transfer between the cloud and on-premise systems. It provides access to the latest GPUs and ensures optimal resource utilization, saving costs and improving performance.

With AI as a service, organizations can scale their infrastructure seamlessly, paying only for what they use. The centralized management and support provide peace of mind, allowing data scientists to focus on their work without worrying about infrastructure maintenance and availability. AI as a service also offers flexibility and agility, allowing data scientists to rapidly deploy and iterate on models.

Getting Started with Core Scientific AI-as-a-Service

To get started with Core Scientific's AI-as-a-Service solution, reach out to Pure Storage at purestorage@corescientific.com. The process involves a simple GitHub login, and within seconds, data scientists will have access to an AI infrastructure platform. This trial allows users to experience the performance and capabilities of Core Scientific's infrastructure firsthand.

In conclusion, AI infrastructure plays a pivotal role in the successful deployment and management of AI initiatives. Consolidating AI workloads, leveraging the latest hardware, and adopting AI-as-a-Service solutions are key strategies for organizations aiming to harness the full potential of AI. By overcoming the challenges of shared infrastructure and embracing innovative solutions, businesses can thrive in the evolving landscape of data science.