COMMISSIONED Enterprises adopting AI to stay competitive must tailor AI models to their needs. This means defining use cases, choosing workflows, investing in the right infrastructure, and partnering for success.
Amidst today’s intense market competition, enterprises seek to leverage AI to gain a strategic advantage. Developing proprietary AI models enables companies to tailor solutions to their unique needs, ensuring optimal performance and differentiation. Starting a project to develop AI models involves navigating a complex landscape of technological challenges and requires careful planning, problem-solving skills, and a strategic approach to AI integration.
In AI development, defining a clear use case is the initial critical step, followed by selecting an AI workflow that ensures efficiency and effectiveness, with tools that are simple, integrated, customizable, scalable, and secure. Performance sizing is key, involving benchmarking and optimizing AI models for speed and accuracy, while balancing other performance metrics. The infrastructure to support AI is extensive, requiring robust data storage, compute resources, data processing, machine learning frameworks, and MLOps platforms. And with investments in AI predicted to reach nearly $200 billion by 2025, the economic potential is significant and necessitates substantial capital investment. Not to mention, the specialized knowledge required for AI projects often necessitates enlisting external expertise.
Each of these challenges must be carefully considered and addressed to ensure the successful development and deployment of AI models. The following step-by-step approach can help organizations address these challenges.
Step 1: Define your use case
Deploying a Generative AI (GenAI) system successfully involves a series of strategic steps, the first and most crucial being defining a clear use case. This foundational step is about understanding the specific needs and objectives of the business, which will guide the selection of the appropriate GenAI workflow. It’s essential to consider the parts of the organization that will be impacted, identify the end-users, and locate where the necessary data is stored.
Aligning GenAI’s capabilities with business goals, whether it’s generating marketing content, providing digital assistance on a website, creating synthetic data or images, or facilitating natural language code development, helps to ensure that the technology is applied in a way that adds value and drives innovation. The success of GenAI deployment hinges on this alignment, resulting in technology that serves as a powerful tool to enhance business processes, engage customers, and foster growth.
Step 2: Choose your AI workflow
Choosing the right AI workflow is crucial for the success of any AI-driven project. Starting with a clear understanding of the objective and the specific use case will guide selection of the appropriate workflow pattern.
Pre-trained models offer a quick start, as they are ready-made solutions that work out-of-the-box for a variety of tasks. Model augmentation, such as retrieval augmented generation (RAG), involves adding new knowledge to an existing model, allowing it to make informed decisions based on additional data. Fine-tuning is a more in-depth process, where the model’s existing knowledge is refined to improve its performance on specific tasks. Finally, model training from scratch is the most comprehensive approach, involving the creation of a new neural network tailored to the unique requirements of the task at hand. This step-by-step escalation in AI workflow complexity, while requiring additional time and effort to complete, allows for a tailored approach that aligns with the project’s goals and technical needs.
Step 3: Size performance requirements
When planning for AI deployment, sizing performance requirements is critical. The type of model you choose, whether it is a language model like GPT4 or an image-based model like DALL-E and Stable Diffusion, influences your compute and storage needs. Language models, while having a high number of parameters, are more compact, which means they require less storage space but more computational power to process a large number of parameters.
On the other hand, image-based models may have fewer parameters but require more storage due to the larger size of the model itself. This distinction is important because it affects how you architect your system’s infrastructure. For instance, a system designed for language models should prioritize processing power, while one for image-based models should focus on storage capabilities. Compute and storage requirements will vary depending on a model’s architecture and the task it is designed to perform so this needs to be factored into how you architect your entire AI project. Understanding these nuances can lead to more efficient resource allocation and a smoother AI workflow.
Common storage solutions for AI models include many options, each with unique benefits and best use cases. Local file storage is often used for smaller, individual projects due to its simplicity and ease of access. Network-attached storage provides more robust solutions for larger datasets, offering better performance and scalability. Distributed file systems (DFS) are ideal for large datasets that require high availability and fault tolerance, as they distribute the data across multiple machines. Object storage is another choice, especially for cloud-native applications, due to its scalability and performance with substantial amounts of unstructured data. It is important to consider the specific needs of your AI model, such as the size of the model and the number of parameters, to choose the most suitable storage solution.
Step 4: Right size your infrastructure investments
Right-sizing infrastructure investments is a critical step in developing efficient AI systems. It involves selecting the appropriate hardware that aligns with the computational demands of the AI models. For instance, smaller AI models may be able to run on optimized laptops such as Dell Precision workstations, while more complex algorithms require powerful setups, such as those with multiple GPUs like Dell’s XE9640 and XE9680 servers. Dell PowerScale offers a versatile storage solution that caters to various needs, from all-flash arrays designed for high performance to tiered storage that balances cost and scalability.
The main advantages of PowerScale for GenAI applications include its scalability, which allows starting with a small and economical setup that can grow exponentially across different environments. It also offers universal data access which allows data to be ingested, read, and written through multiple protocols. Additionally, PowerScale supports GPUDirect, allowing for high-speed and efficient data access, crucial for intensive tasks like AI training. With high-performance Ethernet and NFS over RDMA, it provides for rapid data collection and preprocessing. Lastly, its multicloud deployment capability is essential for running AI workloads in various settings, whether on-premises, at the edge, or in the cloud, providing flexibility and efficiency in AI infrastructure.
Step 5: Engage Dell resources for help
Engaging Dell resources can significantly streamline the process of integrating advanced technologies into your business operations. With step-by-step guidance, your teams can concentrate on strategic growth and innovation rather than the intricacies of implementation. Dell’s Validated Designs and Reference Architectures provide a solid foundation for building efficient IT solutions and assurance that your infrastructure is optimized for performance and reliability. Additionally, we work with our Dell partners to offer specialized AI Workshops which are designed to bring your team up to speed on the latest in AI developments and applications. For a more tailored approach, Dell Professional Services for GenAI offer expertise in deploying generative AI, helping you to quickly establish a robust AI platform and align high-value use cases to drive tangible business value.
In order to be successful with AI model implementation, you need clear guidance on defining use cases, ensuring that your AI initiatives are aligned with strategic business goals. Our Dell AI solutions are designed for efficiency and effectiveness, featuring tools that are not only simple and integrated but also customizable and scalable to meet the evolving demands of AI projects. Performance sizing resources and best practices available through Dell are streamlined with our advanced benchmarking and optimization capabilities, enhancing the speed and accuracy of AI models. The infrastructure required for AI is robust and extensive, and our solutions encompass high-performance data storage, powerful compute resources, and sophisticated data processing capabilities. Recognizing the need for specialized knowledge, we connect you with industry experts to bridge any gaps in expertise, ensuring that your AI projects are not only successful, but also cutting-edge.
To learn more about how Dell storage can support your AI journey visit us online at www.dell.com/powerscale.
Scott Delandy is an engineering technologist at Dell Technologies.
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