Interviewer: Today, we have the pleasure of speaking with Mr. Jivtesh, an expert in cloud computing and AI, who has worked with industry giants like Cisco Systems and IBM. Welcome, Mr. Jivtesh.
Mr. Jivtesh: Thank you for having me. It’s a pleasure to be here.
Interviewer: So, lets begin with something about the impact of Cloud? How has cloud computing contributed to the current AI revolution?
Mr. Jivtesh: Cloud computing has been a game-changer for AI in several ways. For researchers, the cloud provides a convenient and cost-effective way to research, train, and build AI models without the need to maintain and build their own data centers. Imagine trying to build a skyscraper with just a hammer and nails—cloud computing gives you the cranes and bulldozers you need. It allows for easy elastic scalability, access to massive datasets, and the appropriate hardware for both building and running AI tools. Plus, the cloud enables easier collaboration between different research teams since it has global scale and is accessible from anywhere. It’s like having a virtual office where everyone can brainstorm together, no matter where they are.
Interviewer: That’s fascinating. Can you elaborate on the factors related to data that has impacted AI?
Mr. Jivtesh: Absolutely. AI algorithms have existed for quite some time, but the recent boost in their capabilities is largely due to access to the massive amounts of data available on the Internet. Often, an advanced model trained on a smaller dataset performs worse than a simpler algorithm trained on a massive dataset. It’s like trying to become a master chef with just one recipe—more data means more learning opportunities. The cloud facilitates this by providing the infrastructure needed to store and process these large datasets efficiently.
Interviewer: IT does seem the cloud has helped a lot in AI development. What are some of the other benefits you think it brings to AI?
Mr. Jivtesh: The distributed nature of the cloud allows for massive parallelization, which greatly reduces the training time for AI models and the latency in their operation. This means that complex models can be trained faster and deployed more efficiently, leading to quicker insights and more responsive AI applications. This would sound like a simple thing but its impact is huge considering the amount of things it saves. Think of it as having a team of chefs working on different parts for a grand buffet, the more synchronicity, the quicker and more efficient the output.
Interviewer: While we were chatting before the interview began you mentioned that cloud and AI are symbiotic. I know the interview had not started then but I have been curious about those words since then. Can you explain why you said that?
Mr. Jivtesh: Certainly. The growth of cloud computing drives the growth of AI and vice versa. As AI models become more advanced, they require more computational power and storage, which the cloud provides. Conversely, as cloud infrastructure becomes more robust, it enables the development of even more sophisticated AI models. This symbiotic relationship ensures that advancements in one technology will spur advancements in the other.
Interviewer: The other day I saw a video where people mentioned how analytics have changed since AI models came. What are your thoughts about this?
Mr. Jivtesh: With the cloud, AI models can reside where their data already resides, enabling greater analytics and insights on already available data. Even though it might seem trivial, this proximity to data reduces latency tremendously and allows for real-time processing and analysis. And you know every business Analyst would want these crucial applications to have a quick response as these are about real-time decision-making.
Interviewer: Let’s try to give some credit to the AI also for the growth of cloud. How has AI impacted the cloud?
Mr. Jivtesh: AI has significantly influenced cloud architecture. Cloud platforms like AWS, Azure, and Google Cloud have integrated AI models and capabilities, encouraging more businesses to move to the cloud to run their advanced AI models. This integration creates a virtuous cycle where the data generated by these businesses helps improve the AI models themselves. Additionally, cloud architectures have had to evolve to support AI by enabling massive parallelization and including specialized hardware like GPUs and custom chipsets. These components are essential for training and running AI models efficiently, and their inclusion in cloud infrastructure has been a critical development.
Interviewer: Can AI assist in the development and maintenance of cloud platforms?
Mr. Jivtesh: Yes, AI plays a crucial role in developing, running, and debugging the computer code for cloud platforms. AI code assistants like Microsoft’s Copilot, ChatGPT, and Claude help developers write more efficient code, identify bugs, and optimize performance, making the development process more streamlined and effective. These AI tools can analyze vast amounts of code quickly, providing suggestions and improvements that would take human developers much longer to identify.
Interviewer: These seem better predictive abilities, right?
Mr. Jivtesh: Yes, it is that indeed.
Interviewer: So, how does AI’s predictive capabilities benefit cloud providers?
Mr. Jivtesh: It helps cloud providers forecast demand for their infrastructure. This includes determining where and how many switches, routers, CPUs, and other components to deploy based on future demand forecasts. Accurate predictions ensure that cloud providers can scale their infrastructure efficiently and meet customer needs without over-provisioning. This not only saves costs but also ensures that resources are available where and when they are needed most.
Interviewer: These sounds great. How about other areas like security within cloud environments? What are the benefits there?
Mr. Jivtesh: AI is increasingly used for anomaly detection in logs and network traffic to identify leaks and security threats. By analyzing patterns and detecting deviations from the norm, AI can quickly identify potential security issues and alert administrators, helping to maintain the integrity and security of cloud environments. This proactive approach to security is essential in today’s digital landscape, where threats are constantly evolving.
Interviewer: Finally, how has AI contributed to the automation of cloud operations?
Mr. Jivtesh: AI has enabled increased automation of various processes and cloud operations, reducing costs and the need for human intervention. Tasks such as resource allocation, load balancing, and system maintenance can now be automated using AI, leading to more efficient and reliable cloud services. This automation allows cloud providers to offer more consistent performance and uptime, enhancing the overall user experience.
Interviewer: Thank you, Mr. Jivtesh, for sharing your insights. Certainly the interplay between cloud computing and AI sounds so fascinating. It’s been an enlightening and enjoyable conversation.
Mr. Jivtesh: Thank you for having me. It’s been a pleasure discussing these exciting developments. I just hope that interviews like these make people realise how the world of technology will look in the future and how important cloud is going to be in the time to come.
