In an interview with TimesTech, Rahul Jain, Assistant Professor at UPES Online, discusses how AI and automation are revolutionizing cloud services. From predictive maintenance and smart orchestration to real-time decision-making and enhanced security, he highlights how industries like healthcare, finance, and education are benefiting from these intelligent cloud innovations.
Read the full interview here:
TimesTech: How are AI and automation evolving beyond cloud management to enable predictive maintenance and smart orchestration?
AI and automation now enable predictive maintenance by analysing sensor data and historical patterns to anticipate equipment failures, reducing downtime by up to 50%. In cloud orchestration, AI-driven systems autonomously optimize resources, self-heal during disruptions, and dynamically adjust workloads without human intervention. For example, Oracle’s AI-infused platforms predict infrastructure bottlenecks and reroute traffic automatically.
TimesTech: How do AI-driven cloud tools improve scalability, resource optimization, and cost efficiency?
AI-driven cloud tools boost scalability by auto-adjusting resources (like servers or storage) in real time based on predicted demand spikes. This prevents over-provisioning (wasting resources) while keeping performance stable. They optimize costs by using machine learning to analyse past usage patterns and system behaviour, smartly distributing computing power to match needs. For example, predictive scaling spins up servers before traffic surges. These tools also cut costs by shutting down idle resources or switching to cheaper options (like spot instances) during low demand.
TimesTech: In what ways is automation reducing human error and enhancing cloud security?
AI-driven cloud tools boost scalability by auto-adjusting resources (like servers or storage) in real time based on predicted demand spikes. This prevents over-provisioning (wasting resources) while keeping performance stable. They optimize costs by using machine learning to analyse past usage patterns and system behaviour, smartly distributing computing power to match needs. For example, predictive scaling spins up servers before traffic surges. These tools also cut costs by shutting down idle resources or switching to cheaper options (like spot instances) during low demand.
TimesTech: How can machine learning models in the cloud drive real-time smart decision-making?
Machine learning models in the cloud enable real-time smart decision-making by analysing vast amounts of data quickly and accurately. They provide predictive analytics, automate resource scaling, and enhance security by detecting anomalies and potential threats. Cloud-based ML platforms offer scalable resources and services, making it easier for organizations to deploy and benefit from advanced AI capabilities.
TimesTech: How is AI improving hybrid cloud orchestration, especially in sensitive industries like healthcare and finance?
AI improves hybrid cloud orchestration by providing dynamic resource allocation, real-time data flow management, and enhanced security monitoring. In healthcare, AI supports operational efficiency, clinical workflows, and patient care by automating tasks and surfacing data insights. In finance, AI enhances data security and compliance, ensuring sensitive information is protected while optimizing resource use.
TimesTech: Can you share real-world examples of AI transforming cloud services in sectors like education or healthcare?
In healthcare, Mayo Clinic’s partnership with Google Cloud reduced time to diagnose rare diseases from weeks to minutes by analysing medical images with AI. Pfizer used AWS’s computational power to simulate millions of molecular combinations for COVID treatments.
In Education, AI-powered platforms like Coursera and Khan Academy leverage the cloud to deliver adaptive learning experiences. As students engage with courses, AI models track their progress and adjust content difficulty based on performance.
