Mastering AI workflows: How to overcome hybrid cloud challenges

Did you know that 80% of AI workflows rely on hybrid cloud environments, but most businesses struggle to connect their data seamlessly? If you’re an enterprise IT leader or a business decision-maker, you’ve likely faced the challenges of siloed data, prohibitive cloud costs, and the sheer complexity of managing AI workloads. Forget about AI transformation. You’re just trying to play AI catch-up.

But what if there was a way to bridge the gap? Let’s discuss how to optimize your on-premises infrastructure and the cloud to create a seamless, secure workflow for your AI projects.

AI workloads inherently require hybrid cloud environments. Data gravity, latency requirements, and cost considerations often call for a blend of on-premises data centers and public clouds. For example, a business might struggle to train AI models in the cloud due to data silos or latency, slowing progress and reducing efficiency.

NetApp’s unified control plane simplifies these hybrid cloud workflows by providing effective data mobility and visibility. Whether you’re moving data from on-prem to the cloud or managing it across multiple environments, we ensure your AI workloads are optimized.

In this new era of AI and cloud, there’s no room for mediocrity. NetApp’s expertise in cloud storage is unparalleled. As a trusted partner natively embedded in AWS, Azure, and Google Cloud, NetApp is the go-to choice for the world’s largest cloud providers. This deep integration ensures that your hybrid cloud strategy is built on a foundation of reliability, security, and performance.

Consider the story of Nebul, a company that leveraged NetApp to streamline its hybrid cloud AI workflows. By integrating NetApp solutions, Nebul achieved seamless data mobility and improved AI model training efficiency, setting a new standard in its industry. According to Nebul, “NetApp’s solutions have reduced our data transfer times by 50%, allowing us to accelerate our AI development cycles significantly.”

For your organization, this kind of cloud and AI leadership translates into a robust infrastructure that supports your AI initiatives without compromise.

There is no sugarcoating the fact that 85% of AI projects fail, according to IDC. Why? Limitations such as siloed systems, outdated infrastructure, cost constraints, and weak security keep businesses stuck in proof-of-concept mode. Let’s break these down.

Challenge 1: Data silos and lack of mobility

AI projects often stall due to data trapped in silos, unable to move freely between on-premises and cloud environments. We tackle this head on by enabling seamless data movement with solutions like NetApp Cloud Volumes ONTAP. It’s built to manage and migrate your data confidently, giving your AI models access to the necessary datasets they need – anywhere they reside.

Challenge 2: Rising costs and inefficiencies

We know that cloud costs can quickly spiral out of control, especially with the intensive data requirements of AI workloads. We face this ourselves, which is why our cost-optimization features, such as tiering and efficient storage management, can absolutely help you control costs. By automatically moving less frequently accessed data to lower-cost storage tiers, you can get the most out of your cloud investment.

Challenge 3: Security concerns

Security concerns are a daily headache for any IT leader. Cloud environments introduce unique risks. Our approach is to protect your data across hybrid and multi-cloud environments with industry-leading security features, including data encryption, access control, and ransomware protection. We use machine learning to detect ransomware by analyzing data access patterns and anomalies, offering autonomous protection. There are a host of security features integrated into our cloud solutions to give you the confidence that your data is secure, enabling you to focus on driving cloud and AI innovation.

Take the example of Cree8, a company that faced significant security concerns while managing its AI workloads. By partnering with NetApp, Cree8 was able to implement robust security measures for its cloud-native platform, ensuring its data was protected at all times: “With NetApp, we have peace of mind knowing our data is secure, and we can focus on what we do best—innovating.”

Actionable takeaways to optimize AI workflows

To address the challenges of AI workflows and harness the power of hybrid cloud, consider these practical steps:

1. Evaluate your current AI workflows: Identify where data silos or inefficiencies exist.
2. Explore hybrid cloud solutions: Look for options that offer seamless integration and data mobility.
3. Partner with a trusted provider: Ensure your AI strategy is scalable and future-proof.

AI workflows thrive in hybrid cloud environments, and NetApp is the true hybrid cloud leader for data.

Ready to make that AI project a success and transform your infrastructure? Schedule a consultation or explore our hybrid cloud solutions to see why we are the trusted leader in cloud and AI.

For more information, visit NetApp Cloud Services, NetApp Data Services, and NetApp Cloud Storage.