Multi-Cloud & AI: Are You Ready For The Next Frontier? Tech Transformed podcast

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Multi-Cloud & AI: Are You Ready for the Next Frontier?

1d ago 23:45

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"AI may be both the driver and the remedy for multi-cloud adoption," says Dmitry Panenkov, Founder & CEO of emma, alluding to the vast potential and possibilities Artificial Intelligence (AI) and multi-cloud strategies offer.

In this episode of the Tech Transformed podcast, Tom Croll, a Cybersecurity Industry Analyst and Tech Advisor at Lionfish, speaks to Panenkov. They talk about the intricacies of powering multi-cloud systems with AI, offering valuable insights for businesses aiming to tap into the full potential of both.

They also discuss data fragmentation, interoperability issues, and security concerns.

AI Adoption in Multi-Cloud

Addressing the key challenges of AI adoption in multi-cloud environments, Panenkov spotlights one of the most prominent issues – data fragmentation.

“AI thrives on unified data sets. But multi-cloud setups often lead to data silos across the different platforms,” the founder of emma, the cloud management platform, explained.

Data silos creates a disconnect which makes it increasingly challenging for AI models. It makes it harder for AI models to access and process the huge amounts of data needed to function efficiently.

Instead, Panenkov stresses the potential of AI to drive multi-cloud adoption by optimising workloads and automating policies.

In addition to data fragmentation, the lack of interoperability and tooling presents another challenge when integrating AI with multi-cloud. This is where Inconsistent APIs, a lack of standardisation, and variations in cloud-native tools create major friction. The difference is evident when building AI pipelines across diverse environments.

Panenkov also pointed out the impact of latency and performance. He says, "Even Kubernetes is sensitive to latency. When we talk about AI and inference, and I'm not even talking about the training, I'm saying that inference is also sensitive."

Without proper networking solutions, running AI workloads effectively in multi-cloud environments becomes next to impossible.

Of course, security and compliance are a looming challenge for all enterprises across varying industries. Managing data protection in different jurisdictions and environments adds layers of legal and operational complexity.

Despite these challenges, AI has significant advantages in multi-cloud systems that well surpass any challenges.

Intelligent Orchestration is the Key to Successful Multi-Cloud Adoption

The main topic of the conversation was how AI can actually help overcome the complexities of multi-cloud adoption. As the founder of a cloud management platform, Panenkov believes that AI may be both the driver and the remedy for multi-cloud adoption.

For example, Panenkov describes how AI can orchestrate workload placement and resource allocation. “We can look into and predict the behaviour of the workload, and we can optimise the infrastructure for these workloads, and allocate resources, helping our customers to start scaling.”

The promise of portability offered by platforms like Kubernetes is often "undercut by vendor-specific customisations," noted Panenkov, and AI can address this through intelligent orchestration.

AI can enhance workload placement, resource allocation, and even latency routing.

Additionally, AI can automate policies, enforcing security, compliance, and cost rules across various clouds. This "simplification" is critical. Panenkov drew a parallel to the way unified interfaces abstract complexity for users, stating, "All these AI agents have to abstract, evade the complexity for the developers and operators alike."

The impact on business is significant: faster product delivery, accelerated innovation, and the ability to leverage "best of breed" tools without heavy vendor lock-in. This leads to higher agility in scaling and adapting applications to market needs more quickly.

Takeaways

AI thrives on unified data sets.
Data fragmentation leads to silos in multi-cloud environments.
Kubernetes can complicate multi-cloud strategies due to latency issues.
Edge computing adds complexity but offers low-latency benefits.
AI can optimise workload placement and resource allocation.
AI helps enforce security policies across multiple clouds.
A multi-cloud strategy can reshape how organisations innovate.
Organisations can mitigate risks by avoiding vendor lock-in.
Building intelligent abstraction layers is crucial for flexibility.
Leaders should focus on orchestration and not force-fit clouds.

Chapters

00:00 Introduction to AI and Multi-Cloud Challenges

02:31 Key Challenges in Multi-Cloud AI Adoption

11:43 AI as a Driver for Multi-Cloud Adoption

16:33 Impact of Multi-Cloud on Innovation and Risk Management

23:00 Final Thoughts and Advice for Leaders

About Emma

emma helps organisations use real multi-cloud options and unlimited computing power to get the most from their cloud and AI efforts. With support from various providers, including hyperscale and EU-regional, and environments such as on-prem, private, and public, emma enables organisations to reach their business goals now and in the future.

Unlike solutions that focus on just certain parts, emma takes a complete approach to cloud management. By combining AI-driven performance, cost, and network management with tools for optimisation and governance, emma delivers a smart platform that makes cloud operations easier and boosts efficiency.

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