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**What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is the broad field of building systems that can mimic human intelligence—such as reasoning, problem-solving, perception, and decision-making. It is an umbrella term that includes everything from rule-based systems to advanced learning models.
In simple terms:
AI is about building systems that can make decisions or predictions like humans
As a Cloud/DevOps engineer, think it like a highly sophisticated load balancer. A standard load balancer follows hardcoded rules (Round Robin, Least Connections). An "AI Load Balancer" would analyze traffic patterns, predict surges based on external events, and reconfigure itself without manual intervention.
What is Machine Learning (ML)?
Machine Learning (ML) is a subset of AI where systems learn patterns from data instead of being explicitly programmed with rules. Instead of writing logic manually, you feed data into algorithms that improve over time.
In simple terms:
** ML is a subset of AI where systems learn patterns from data instead of being explicitly programmed.**
As a Cloud/DevOps engineer, think it like an intelligent Auto Scaler, which is a Auto-scaling system that learning traffic patterns instead of rule based pattern like:
** if traffic > 1000 → scale or average cpu > 70 etc **
As a DevOps engineer , we often need to build ML Model pipelines , in that case use the below reference for understanding:
The Data = Your "Source Code.
The Training = The "Build Process." You run the data through a trainer to produce a Model.
The Model = The "Artifact" or "Binary." Just like you deploy a .jar or a Docker image, you deploy an ML model to production to handle live requests.
So, ML is about automating the creation of logic. You manage the pipeline that produces the artifact.
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