IT Automation: a key enabler for enterprise AI adoption

Having spent my career in the technology industry, I've had the opportunity to experience major shifts in the field through my work with customers. Specifically in the last decade, my projects have consistently involved at least one of three trends: advanced data analytics/artificial intelligence, automation and IoT/edge computing. It’s fascinating to observe how these areas continue to converge, transforming all industries by enabling smarter, more efficient, real-time decision-making.

AI is vital for companies to enhance efficiency, drive innovation and improve customer satisfaction. IT environments must remain both reliable and consistently accessible to support these critical models. To achieve this, organizations can rely on automation as a key component of enabling AI, as it guarantees the uptime and efficiency needed to support these workloads effectively. In this blog series, I’ll explore how IT automation, and in particular Red Hat Ansible Automation Platform, can serve as a foundational element for successful AI implementations.

Preparing your infrastructure for AI workloads

At the core of any AI-driven initiative is the processing of vast volumes of data. AI models rely on data to learn, evolve and improve. A major factor in the successful deployment of AI is ensuring uptime and availability of the supporting infrastructure. Any disruption in uptime not only affects productivity, but can also disrupt decision-making, harm customer experiences and even impact financial results. This makes reliability and availability more crucial than ever.

Upgrading IT infrastructure to prepare for AI workloads involves elements such as ensuring sufficient power and cooling, preparing the network to handle large data volumes, optimizing and expanding capacity, and implementing security measures, while enabling scalability.

Given the critical importance of uptime, ensuring high availability for systems and applications is essential for businesses utilizing AI. This requires a blend of several important components: 

• Reliable infrastructure that is capable of handling immense amounts of data to ensure operations remain uninterrupted.
• Robust failover strategies to prevent operational disruptions.
• Continuous monitoring to mitigate risks, maintain smooth performance, and detect and resolve potential issues before they cause outages.

Uptime and availability are not merely technical concerns; they are foundational to AI-driven business operations.

The role of IT automation

AI workloads place significant strain on IT teams to build, secure, scale and maintain the existing as well as new infrastructure needed for AI. As AI models become more complex and data volumes continue to rise, there is an increasing need for AI-optimized infrastructure, whether on-premise or in the cloud. With the growing demand for custom model training, businesses require greater computing power, network bandwidth and storage capacity. New systems must be deployed, configured and maintained to ensure they are always available to the AI/ML engineers and data scientists.

As the demands on IT systems grow, IT automation enhances the effectiveness of IT teams, minimizes human errors, supports ongoing improvement and management, and accelerates AI development. Automation can be viewed as the foundation of AI, as it guarantees the reliability and efficiency needed for AI workloads. By handling the management of critical infrastructure elements such as operating systems, networks, storage, data and applications, automation enables optimal performance and seamless integration. It also empowers teams to efficiently manage resources such as accelerators like GPUs (including installation and updates) and the flow of data to and from the edge. Furthermore, automating IT infrastructure can save both time and money, freeing up resources to support ongoing AI innovations.

The value of Ansible Automation Platform

Ansible Automation Platform plays a critical role in establishing a solid foundation for AI implementations by simplifying the deployment, management, configuration, lifecycle of models  and AI infrastructure components. Here’s how:

Standardized deployment. Ansible Playbooks provide a consistent and repeatable method for deploying AI components like operating systems, servers, storage, models, containers, data and networking resources. By codifying the infrastructure as code, Ansible Automation Platform promotes uniformity and reliability across all AI environments, reducing the likelihood of configuration errors or discrepancies.

Monitoring and alerting integration. Ansible Automation Platform integrates seamlessly with monitoring and alerting tools, allowing IT operations teams to automate the setup of monitoring agents, thresholds and alerting rules for AI infrastructure components. By continuously tracking performance metrics and system health, Ansible Automation Platform helps identify and address potential issues proactively, preventing disruptions to AI operations.

Data management. One of the most difficult tasks for training AI models is getting the data from where it is created into a location where it can be trained. Ansible Automation Platform is key to not only helping the movement of data from servers to storage in region, but also making sure the data is accessible to the correct users for training the models using Red Hat OpenShift AI.

Summary

As organizations seek to modernize and position themselves for AI-driven opportunities and transformation, they are confronted with the growing complexity of today’s IT systems. For many, this challenge can lead to confusion and hinder efficiency, resulting in missed opportunities for competitive advantage. It’s imperative for enterprises to cut through the noise to gain a strategic edge in the marketplace.

Red Hat Ansible Automation Platform offers a solution designed to streamline processes, save time, enhance quality, empower teams and manage costs effectively. Ansible Automation Platform components contribute to building a robust foundation for AI implementations by enabling standardized deployment, scalability, configuration management, high availability, monitoring integration, disaster recovery, version control and documentation. By automating routine tasks and enforcing best practices, Ansible Automation Platform helps with the reliability, performance and resilience of AI infrastructure in IT operations.

Next up

In the next blog in this series, we will explore three pillars of automation use cases in the AI realm: orchestration with AIOps, operationalization and infrastructure optimization. Discover how to fully utilize the AI features built into the technology vendors’ solutions already within your infrastructure. Additionally, we’ll dive into a use case involving the Red Hat AI portfolio that results in a fully self-healing infrastructure. We’ll explain how to use automation to streamline the onboarding of new edge deployments, such as IoT devices, to collect and coordinate their data with AI solutions.

Call to action

Join this webinar IT Automation: A key enabler for enterprise AI adoption