10 Questions
What is a key characteristic of cloud computing?
It provides scalable computing resources on-demand
What is the primary goal of machine learning?
To mimic human intelligence and behavior
Which of the following is a benefit of cloud computing?
Pay-per-usage of the resources
What is deep learning?
A subfield of machine learning
What is supervised learning?
Training a model with labeled data
What is reinforcement learning?
Training a model using trial and error
What is the primary deployment model for cloud computing?
All of the above
What is a Service Level Agreement (SLA)?
A contract between a cloud provider and a customer
What is clustering?
A type of unsupervised learning
What is a key benefit of cloud computing regarding maintenance?
It reduces the need for maintenance
Study Notes
Cloud Computing
- Cloud computing provides flexible self-service, network-accessible computing resource pools that can be allocated to meet demand.
- Offers three service models: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service).
- Deployment models include Private, Public, and Hybrid.
Characteristics of Cloud Computing
- Scalability: Resources can be quickly scaled up or down to match changing business needs.
- Storage: Large amounts of data can be stored and accessed online.
- Security: Data is protected through robust security measures.
- Benefits include reduced data loss, minimal maintenance, and pay-per-usage of resources.
- Accessibility: Resources can be accessed from anywhere, at any time, and from any device.
Service Level Agreements (SLA)
- Defines the level of service expected from a cloud provider.
Machine Learning (ML)
- Artificial Intelligence (AI): Capable of mimicking human intelligence and behavior.
- ML: A process used to achieve AI, involving designing algorithms that learn from data to become more accurate and effective over time.
Categories of Machine Learning
- Supervised Learning: Involves training models on labeled data to make predictions.
- Unsupervised Learning: Models learn from unlabeled data to identify patterns.
- Reinforcement Learning: Models learn from external interactions and improve with time.
Types of Supervised Learning
- Classification: Predicting a categorical output (e.g., yes or no).
- Regression: Predicting a continuous output (e.g., a numerical value).
Applications of Machine Learning
- Widely used in gaming, robotics, industrial automation, healthcare, and online stock trading.
Test your knowledge of cloud computing concepts, including deployment models, service models, and benefits of cloud computing.
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