Artificial intelligence (AI) and machine learning (ML) are often spoken about in the same breath. Because of their close relationship, the discussion around “AI vs. ML” is less about drawing sharp boundaries and more about understanding how they connect and complement each other.
What is Artificial Intelligence (AI)?
Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning, reasoning, and problem-solving. Through AI, machines use mathematics and logic to simulate human-like decision-making.
AI can be found in everyday life—from voice assistants like Siri and Alexa to recommendation engines on Netflix and Spotify. The unifying factor across all these applications is that the machine is performing tasks we normally associate with human intelligence.
What is Machine Learning?
Machine learning is a subset of AI. It's the process of using mathematical models of data to help a computer learn without direct instruction. Instead of following explicit rules, ML systems improve on their own as they gain experience.
One of the most powerful methods in ML is the neural network, a series of algorithms modeled after the human brain. Neural networks allow machines to handle complex tasks like image recognition, natural language processing, and self-driving car navigation. When these networks become deeper and more complex, we call it deep learning.
Are AI and Machine Learning the Same?
Not quite. AI is the broad field of creating “intelligent” systems, while ML is one of the most successful techniques within that field. Put simply:
- AI is the concept of machines being smart.
- ML is one way to make machines smart.
This distinction is why discussions about AI and ML often feel like they're two sides of the same coin.
How are AI and Machine Learning Connected?
An “intelligent” computer uses AI to think like a human and perform tasks autonomously. Machine learning is the process that allows the computer to develop that intelligence. The two are interdependent—AI provides the vision, while ML provides the method.
Key Terminologies
To better understand the world of AI and cloud computing, it helps to know some foundational terms. Below are definitions of commonly used concepts, adapted from Microsoft's Cloud Computing Dictionary.
Cloud Bursting
A configuration between a private cloud and a public cloud to manage demand for cloud resources. When a private cloud reaches full capacity, overflow traffic is automatically directed to the public cloud.
Example: A retail company handling Black Friday traffic spikes without over-provisioning servers year-round.
Computer Vision
A form of AI that enables computers to “see” and interpret images or videos in a way similar to humans. It powers technologies like facial recognition, self-driving cars, and quality checks in manufacturing.
Container
A lightweight unit of software that packages an application with all its dependencies, ensuring it runs consistently across environments. Containers are widely used in DevOps for efficient deployment.
Data Warehouse
A centralized repository for structured and semi-structured data used for reporting and analysis. For instance, retail businesses consolidate sales data from point-of-sale systems and online platforms into a data warehouse for trend analysis.
Data Governance
The set of policies and standards an organization uses to keep data accurate, secure, and private. It's a cornerstone for businesses relying heavily on data-driven decisions.
Data Integration
The process of combining data from multiple sources into a unified view. For example, merging CRM, ERP, and web analytics data for a 360-degree customer view.
Data Lake
A storage system capable of handling structured, semi-structured, and unstructured data. Unlike a data warehouse, it can store raw data like sensor logs, tweets, or images alongside relational tables.
Database Sharding
Splitting a large database into smaller, faster, and easier-to-manage pieces across servers. Used by companies like Instagram and Uber to handle massive user data efficiently.
Deep Learning
A branch of ML where artificial neural networks handle unstructured data and complex patterns. Deep learning powers advanced applications like ChatGPT, medical image analysis, and fraud detection.
Edge Computing
Processing data closer to the source—on a local device or nearby server—rather than sending everything to a central cloud. This reduces latency, making it essential for IoT applications such as autonomous vehicles.
Elastic Computing
The ability to dynamically scale computing resources up or down based on demand. This flexibility is one of the key benefits of cloud platforms like AWS or Azure.
Grid Computing
A distributed computing system where many networked computers act like a single supercomputer. Often used for scientific simulations or large-scale data analysis.
Kubernetes vs. Docker
- Docker: A platform and file format for creating and running containers.
- Kubernetes: Orchestration software that manages how and where containers run.
They are not competitors but complementary tools often used together.
Middleware
Software that acts as a bridge between operating systems and applications, enabling communication and data management. Examples include web servers and application servers.
Virtual Desktop Infrastructure (VDI)
Infrastructure that allows users to access desktops remotely from almost any device. Popular for enabling secure work-from-anywhere policies.
Virtual Private Network (VPN)
A secure, encrypted connection between a user's device and a remote server. VPNs are used for privacy, bypassing geo-restrictions, and secure corporate access.
Virtualization
The process of creating virtual versions of computing environments—servers, storage, or networks—so a single physical machine can run multiple virtual machines efficiently.