AI & Cloud: best practices and examples

AI and cloud technologies are closely linked. Companies are increasingly relying on AI to automate processes, optimize decisions and develop innovative products. At the same time, the demands on infrastructure, scalability and security are increasing in order to operate complex AI workloads efficiently. Managed cloud services offer the perfect solution here: they simplify deployment, speed up processing and allow companies to focus on their core business.

In this article, we show why AI and the cloud make an unbeatable team, the benefits of managed clouds for AI workloads and how companies can make the most of this technology.

Why AI and the cloud belong together

The development and use of AI requires enormous computing power and a flexible infrastructure. Models for machine learning or deep learning require:

• Massive computing resources: GPUs and specialized hardware for training processes.
• Large data storage: scalable storage for unstructured data.
• Flexible infrastructure: Possibility to increase or decrease resources as required.

This is where the cloud comes into play. Cloud platforms such as the NETWAYS Cloud from NWS offer precisely this scalability and computing power. This allows companies to launch AI projects more quickly without having to invest in their own hardware.

Advantages of combining AI and the cloud:

1. Scalability: If demand increases, you can expand your computing capacity immediately.
2. Cost efficiency: Only the resources actually used are charged.
3. Faster time-to-market: You can train, test or implement AI models more quickly.
4. Availability: You can deploy applications quickly without having to set up local infrastructure.

Managed cloud services: Simplifying AI workloads

A managed cloud takes over the administration, monitoring and optimization of the cloud infrastructure. For AI projects, this means

• Automated resource management: servers, storage and network are provided as required.
• Optimized performance: Managed services automatically select the right hardware for AI training or inference.
• Security and compliance management: protection of sensitive data and compliance with GDPR or ISO standards
• Cost control: Transparent billing and optimization of resources prevent unnecessary expenditure.

Managed clouds also mean that companies do not have to set up their own IT teams to operate the AI infrastructure. Instead, they can concentrate on data analysis, model development and innovative applications.

Examples of AI workloads in the cloud

1. Machine learning (ML) and deep learning: Training large models such as GPT or image classifiers requires enormous computing capacities. Managed clouds offer scalable GPU clusters, automatic load balancing functions and optimized storage systems.
2. Data analysis and business intelligence: companies use AI to recognize patterns in large amounts of data. Managed cloud services enable the rapid processing of data streams in real time and seamless integration into analytics tools.
3. Predictive maintenance and IoT: sensors collect large amounts of data in industry. AI models analyze this data in the cloud to make predictions about the condition of the machines. Managed services ensure that these workloads run reliably and scalably.
4. Natural language processing (NLP) and chatbots: Cloud-based AI services enable the processing of voice, text or customer interactions without the need to set up your own infrastructure.

Examples of AI workloads by industry:

1. E-commerce: AI analyzes customer behavior and the cloud ensures scaling during traffic peaks
2. Industry 4.0: Sensor data is stored in the cloud and AI recognizes maintenance requirements
3. Finance: AI-based risk analyses run on cloud servers so that decisions can be made in real time.
4. Healthcare: patient data is stored securely in the cloud and AI models recognize disease patterns

Best practices for the use of AI in managed clouds

To exploit the full potential of AI & Cloud, companies should consider the following strategies:

• Carry out a needs analysis: Which workloads should be operated in the cloud? What computing power is required?
• Compare cloud providers: The providers differ in terms of costs, hardware, service level and compliance.
• Plan for scalability: Plan for scalability, as AI workloads can grow unpredictably. Managed clouds enable elastic resources.
• Implement security guidelines: Data protection and access control are crucial for sensitive data.
• Monitoring: Continuously monitor key performance indicators to reduce costs and increase efficiency.
• Cost optimization in the cloud: AI not only makes decisions in real time, but also reacts to changes in order to reduce costs. It recognizes unused or inefficient processes and dynamically adjusts resource usage. The result is less waste and healthier cloud bills.
• Cloud-first strategy: The cloud-first strategy states that the cloud should be the foundation of any modern IT architecture. The advantages are scalability, global availability and the rapid implementation of AI services.

• Microservices architecture: Microservices enable independent deployments. Containerization with Kubernetes increases flexibility and simplifies updates.
• Develop a data strategy: AI requires structured and easily accessible data. Data architectures must be cloud-friendly.
• Use automation: AI model deployments speed up processes and reduce errors.
• Proactive fault detection: A machine learning model trained to detect atypical metrics in pods, networks or storage can intervene before an incident becomes critical. Kubernetes can restart affected services, reroute traffic or trigger alerts, minimizing the impact on users.
• Training and deployment of AI models on a large scale: Kubernetes enables the execution of distributed training loads across multiple nodes. This accelerates model development and simplifies the transition from the test to the production environment. This shortens development cycles and ensures stability.
• Start with a pilot project: Start with a clearly defined pilot project that promises rapid success. Use this project to validate your solution and gain valuable experience.

Implementing an AI infrastructure is not a sprint, but a marathon. Therefore, start with a thorough inventory of the existing IT landscape and identify gaps that need to be closed for AI workloads.

The future of the cloud: orchestrated and intelligent

The cloud and AI are not separate technologies – they can reinforce each other, contributing to faster, more efficient and more autonomous systems. Together, they enable cloud operations that not only react, but also anticipate and optimize in real time.

The question is no longer whether they should be combined, but how your company can use this convergence to become more agile, save costs and create more value for users.

The future of the cloud is already taking shape: organizations are integrating operational AI to achieve faster deployments, smarter decisions and more resilient operations. They reduce costs without sacrificing performance, detect problems before they occur and adapt to business requirements in real time.

Technologies continue to develop:

• Serverless computing: even more flexible provision of services without having to manage your own infrastructure.
• Edge computing: data processing directly at the point of origin (e.g. IoT devices), AI directly on site.
• Generative AI: Generation of new content, models and services in real time.

Companies that focus on these trends at an early stage secure competitive advantages.

Conclusion: The perfect duo for the future

AI and cloud computing form a powerful duo that enables companies to implement innovative AI applications quickly, securely and scalably. Managed cloud services not only simplify the operation of AI workloads, but also optimize performance, security and costs.

Companies that rely on managed clouds for AI today are laying the foundations for efficient, future-proof data strategies and maintaining their lead over the digital competition.