Change Approach to AI with IBM Storage Scale: 10 Tips for Success

By Ryan Avery, technical solution architect , WWT (World Wide Technology), and

In the race to harness the transformative power of AI, organizations are swiftly adopting advanced solutions to stay competitive. This article offers a roadmap to navigating the complexities of AI integration, spotlighting ten crucial tips for success. From optimizing big data management to leveraging robust infrastructure, these insights are designed to propel your organization to the forefront of the AI revolution.

It’s indeed a transformative time! Generative AI (GenAI) is making significant strides as companies race to deploy these solutions for hundreds of new use cases. The ultimate winners will be the organizations that get AI-infused products into production faster. Companies are rapidly adopting GenAI to capitalize on the promise of significant advancements in numerous key areas including, but not limited to:

Enhancing customer service.
Streamlining software development
Optimizing IT operations.
Improving cost management by identifying inefficiencies and automating routine tasks.

Streamlining data management and enhancing scalability
Modern applications require modern infrastructure, so many organizations are moving towards an ‘AI center of excellence’ approach to make it easier to share data and simplify scalability. To stay competitive, you must develop an AI infrastructure that delivers high performance and scalability, remains easy to manage, and is cost-effective. A software-defined approach that is policy-based can help you achieve all this by providing the right storage systems at the right time and with the right type of media.

To effectively leverage GenAI, you need to share data more easily among data scientists globally. This requires data orchestration, which allows companies to maintain one copy of their data on IBM or non-IBM storage so they can simplify and reduce the number of copies they need to keep. You need enough performance to get the job done, manage costs, and maximize your investments in GPUs and data sciences. Data scientists and GPUs are amongst the most expensive resources that organizations fund. If the data scientists have to wait for data they are not being used cost-effectively. Achieving all this can be extremely challenging – but it can be simpler than you think. To begin with, organizations encounter some common problems when building a GenAI program.

Many face challenges, such as:

Too many copies of data, which are difficult to manage and difficult to grow or scale.
The rising cost of both big data and hyper-scaler bills.
Things just don’t work. For example, your system cannot complete backups in the backup window.
Underutilized compute assets, increasing training and inference time.

The good news is that all these challenges can be solved with firm’s Storage Scale.

Unlocking AI potential with Storage Scale systems
A global data platform is the ideal approach to storage for AI solutions but how do you get started when time is of the essence? The company has a solution with extreme performance in the simplest way possible. Unlike more complex solutions, Storage Scale System offers a high-performance appliance tailored for efficient deployment in your environment. The benefits are vast:

Ease of deployment: There’s no need to build or integrate the system yourself.
Low maintenance: It does not require extensive ongoing maintenance.
Hassle-free upgrades: You can replace a drive dynamically and add/or remove hardware with ease and without downtime.
Enhanced capabilities: Features such as Active File Management, Global Data Platform, and data tiering make it easy to access your data from anywhere.
Scalability: It scales to meet both current and future capacity and performance demands.

Storage Scale software has been refined over 25 years, and the Storage Scale System appliance iteration has been refined for over a decade. The solution is a demonstrated robust big data handler, now adeptly addressing the challenges introduced by GenAI. To this point, Storage Scale offers:

User-friendly operational dashboards: Simplified management and enhanced usability.
Software-defined scalability: It facilitates expansion and management, supporting the creation of a high-performance global data platform that remains simple and cost-effective.
Advanced tiering and automation: It manages costs effectively by tiering data to tape, cloud, or object storage based on needs. This allows for purchasing high performance capacity when needed for tasks like feeding NVIDIA GPUs, and cost-effective capacity for less frequently accessed data to optimize expenses.
Global data sharing: Enhanced collaboration and resiliency by replicating changes over the WAN, making it faster and more cost-effective.
Robust DR options: It offers both synchronous and asynchronous DR to ensure business continuity with fast recovery, which is challenging yet imperative to managing big data at scale.
Certified performance: Recognized for its industry-leading performance, it is certified for integration with NVIDIA’s DGX SuperPOD.

This suite of features enables clients to construct an AI architecture that perfectly balances compute, data, and networking resources, maximizing productivity and efficiency. By optimizing these elements, customers are positioned for success and, more importantly, competitive advantage.

Enhancing AI infrastructure: Flexibility, data access, and sustainability with IBM
Storage Scale System for AI applications not only delivers the required performance and simplicity for GenAI, it also has several other benefits, some of which may be pleasantly surprising:

Flexibility: IBM provides highly competitive and scalable solutions, adaptable for a range of modern AI scenarios. You can:
- Tier data to tape or HDD based on policies.
- Integrate with cloud storage.
- Extend the utility of your investment to support other environments, such as Hadoop clusters or global file shares.
Data access: Fast data retrieval and input, regardless of the platform – Linux, Windows, cloud, object storage, or Hadoop data lakes – makes it a global data platform. This versatility helps eliminate data silos and reduce redundant data copies.
Data resiliency: You can be secure at all levels.
Data abstraction: The ability to sync changes to a data set globally which is used for global collaboration, backup, DR, and archiving at other locations or clouds.
Sustainability: As AI pushes data centers to their limits in terms of power and cooling, IBM’s system stands out for their efficiency.
- AI computing demands substantial power, making solutions that offer high performance per watt increasingly vital.
- The IBM Storage Scale solution requires significantly less power while providing the highest density available in the market.
- Noteworthy performance metrics/TB and kilowatt further highlight its efficiency.

In sum, Storage Scale Systems for AI applications is for organizations with substantial data but lacking a platform to leverage it. This could be particularly important to those eager to begin harnessing AI capabilities such as chatbots or retrieval augmented generation (RAG) environments.

Often, organizations start by tentatively exploring AI, only to discover its flexibility, simplicity, power, and cost-effectiveness. Typically, they initiate the process with a single workload, and as the benefits become apparent, they progressively implement additional workloads.

10 tips to help you win race to AI
Embarking on the journey to integrate AI into your operations can be daunting, but with the right strategies and tools, you can effectively navigate this complex landscape. Below are ten essential tips that will help you not only keep pace but excel in the fast-evolving race to AI, ensuring your organization leverages its full potential.

1. Big data will keep growing, so plan to manage it and get away from limiting yourself to just traditional NAS storage or the cloud.

2. Engage a trusted partner such as WWT. We can help you with:

Business value discussion
Technology discussion
PoC
Design
Implementation assistance
Operational support services
Support
Training
Ongoing lifecycle management of offerings

3. Develop long-term plan for managing big data on a global scale, extending beyond initial quick-start efforts.

4. Prioritize storage and networking at the outset of the design process. While many tend to concentrate solely on computing power, overlooking these critical components can lead to inefficiencies later on.

5. Reconsider tape. A lot of people might think tape is old school, which it is but it still has value. Nothing is cheaper, denser, more secure, consumes less power, or has a longer shelf life. Did you know that Facebook, Google, Amazon, and Microsoft use tape extensively in their data centers for cool data storage?

6. Start by solving more difficult problems with big data and work from there. Showing early wins with a big impact will earn support from the organization and build momentum.

7. Embrace continuous learning and upskilling. As AI technologies evolve rapidly, keeping your team’s skills up to date is crucial. Invest in regular training and development programs to ensure your staff remains on the cutting edge of AI advancements.

8. Consider feedback as an opportunity for improvement. While a small number of individuals in any organization may voice the majority of concerns, statistics show that less than 5% of people account for 40% of all feedback. This may indicate that the infrastructure isn’t optimally designed to meet their performance needs. WWT and IBM are here to help you enhance your infrastructure, remove bottlenecks, and solve any issues, ensuring a more efficient and effective environment.

9. Involve the appropriate business leaders in initial planning of the solution to make sure every use case is considered in advance. A successful deployment requires buy-in from the key players in your organization.

10. Try it out. WWT offers clients its AI Proving Ground, a unique lab environment that accelerates your ability to learn about, test, train, and implement innovative AI solutions. The AI Proving Ground features an unrivaled blend of multi-OEM infrastructure, software, and cloud connectivity – all designed to accelerate decision-making for your AI-powered solutions.

Ready to change your approach to AI? Explore how WWT’s AI Proving Ground and Storage Scale can catapult your AI initiatives to new heights. Learn more about these dynamic resources and sign up for a tailored assessment today to unlock the full potential of AI for your business.