Question 1

What's the difference between Machine Learning and Artificial Intelligence (AI)?

Accepted Answer

Artificial Intelligence (AI) is the broad goal of making computers perform tasks that seem “intelligent,” like understanding language or making decisions. Machine Learning (ML) is one way to achieve AI: instead of writing explicit rules, you train a model on data so it learns patterns and can make predictions or decisions on new data.

Question 2

When should I use Machine Learning?

Accepted Answer

Use machine learning when (1) the problem is hard to solve with fixed rules, (2) you have enough data that represents the real-world situation, and (3) you can clearly define what “good” looks like (a target label or a measurable outcome). Common fits include recommendation systems, fraud detection, demand forecasting, image/text classification, anomaly detection, and personalization. If the logic is simple and stable (e.g., tax calculation rules), traditional programming is usually better.

Question 3

How much does Machine Learning cost?

Accepted Answer

Costs depend on where you run it and what you do. Main factors include: compute for training (CPU/GPU/TPU hours), storage for datasets and model artifacts, data processing/feature engineering, inference (real-time endpoints billed per instance/hour plus requests, or batch jobs billed per compute time), and MLOps/monitoring (logging, metrics, model drift detection). Costs can range from low (small models trained occasionally on CPUs) to high (large deep learning models trained on multiple GPUs). Managed services (e.g., SageMaker, Azure ML, Vertex AI, OCI Data Science) add convenience but you still pay for underlying compute, storage, and network usage.

Machine Learning

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