Starting a project to develop AI models involves navigating a complex landscape of technological challenges. Navigating these challenges 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 the benchmarking and optimization of 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. With investments in AI predicted to reach nearly $200 billion by 2025, the economic potential is significant, necessitating substantial capital. Additionally, due to the specialized knowledge required for AI projects, seeking external expertise is often necessary.

Each of these challenges must be carefully considered and addressed to ensure the successful development and deployment of AI models. Let’s take a step by step look at how to organizations can address these challenges.

Step 1: Define your use case.

Deploying a General AI (GenAI) system successfully involves a series of strategic steps, with the first and most crucial being the definition of 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.

By aligning the GenAI’s capabilities with the 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, you ensure that the technology is applied in a way that adds value and drives innovation. The success of GenAI deployment hinges on this alignment, ensuring that the technology 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. It begins with a clear understanding of the objective and the specific use case, which guides the selection of the appropriate workflow pattern.

Pre-trained models offer a quick start, as they are ready-made solutions that can be used out-of-the-box for a variety of tasks. Model augmentation 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 allows for a tailored approach that aligns with the project’s goals and technical needs.

Step 3: Size your performance requirements.

When planning for AI deployment, sizing performance requirements is indeed a crucial step. The type of model you choose whether it is a language model like GPT-3 or an image-based model like 3D UNet, will significantly influence your compute and storage needs. Language models, while having a high number of parameters, are more compact in size, which means they require less storage space but more computational power to process the 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. Understanding these nuances can lead to more efficient resource allocation and a smoother AI workflow.

Common storage solutions for AI models include a variety of options, each with its own set of 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 provide 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, simpler models may run on standard laptops, while more complex algorithms require powerful setups, such as those with multiple GPUs. 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 General AI (GenAI) applications include its scalability, which allows starting with a small and economical setup that can grow exponentially across different environments. It also provides universal data access, enabling the ingestion, reading, and writing of data through multiple protocols. Additionally, PowerScale supports GPUDirect, which allows for high-speed and efficient data access, crucial for intensive tasks like AI training. With high-performance Ethernet and NFS over RDMA, it ensures rapid data collection and preprocessing. Lastly, its multi-cloud 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, ensuring that your infrastructure is optimized for performance and reliability. Additionally, Dell and its partners 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 offers 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.

Steps to Success: Summary and recap.

From the outset, Dell provides clear guidance on defining use cases, ensuring that your AI initiatives are aligned with strategic business goals. Their AI workflow is 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 is streamlined with Dell’s advanced benchmarking and optimization capabilities, which enhance the speed and accuracy of AI models. The infrastructure required for AI is robust and extensive, and Dell’s solutions encompass high-performance data storage, powerful compute resources, and sophisticated data processing capabilities. Recognizing the need for specialized knowledge, Dell Technologies connects you with industry experts to bridge any gaps in expertise, ensuring that your AI projects are not only successful but also cutting-edge.

Embrace the future of AI with Dell Technologies to learn more about how they can support your AI journey. As your partner, Dell can help transform challenges into opportunities and drive innovation within your organization.