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AWS > Machine Learning > Flashcards

Machine Learning Flashcards

1
Q

Q: What is Machine Learning?

A

A: Machine learning is a subset of artificial intelligence where algorithms learn from data to make predictions or decisions without being explicitly programmed.

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2
Q

Q: What are the key AWS Machine Learning services?

A
  • Amazon SageMaker
  • AWS DeepLens
  • AWS DeepRacer
  • Amazon Comprehend
  • Amazon Rekognition
  • Amazon Polly
  • Amazon Translate
  • Amazon Lex
  • Amazon Transcribe
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3
Q

Q: What is Amazon SageMaker?

A

A: A fully managed service for building, training, and deploying machine learning models at scale.

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4
Q

Q: What is SageMaker Studio?

A

A: An integrated development environment (IDE) for machine learning that provides tools for preparing data, building models, and monitoring experiments.

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5
Q

Q: What is SageMaker Ground Truth?

A

A: A data labeling service that uses machine learning to reduce the cost and time of annotating datasets.

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6
Q

Q: What are AWS Deep Learning AMIs?

A

A: Preconfigured Amazon Machine Images (AMIs) with deep learning frameworks like TensorFlow, PyTorch, and Apache MXNet.

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7
Q

Q: What is Amazon Rekognition?

A

A: A service for image and video analysis, including facial recognition, object detection, and moderation.

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8
Q

Q: What is Amazon Comprehend?

A

A: A natural language processing (NLP) service for extracting insights like sentiment, key phrases, and entities from text.

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9
Q

Q: What is Amazon Polly?

A

A: A service that converts text into lifelike speech using text-to-speech (TTS) technology.

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10
Q

Q: What is Amazon Translate?

A

A: A neural machine translation service for translating text between languages.

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11
Q

Q: What is Amazon Lex?

A

A: A service for building conversational interfaces, such as chatbots, using automatic speech recognition (ASR) and natural language understanding (NLU).

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12
Q

Q: What is Amazon Transcribe?

A

A: A speech-to-text service that converts audio recordings into text.

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13
Q

Q: What are the stages of the machine learning workflow in AWS?

A
  • Data collection and preparation
  • Model building
  • Model training
  • Model evaluation
  • Model deployment
  • Monitoring and maintenance
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14
Q

Q: What is a training job in SageMaker?

A

A: A managed process for training machine learning models on large datasets using built-in or custom algorithms.

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15
Q

Q: What is feature engineering?

A

A: The process of selecting, transforming, and creating features from raw data to improve model performance.

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16
Q

Q: What are the options for deploying models in SageMaker?

A

A: Real-time endpoints, batch transform, or edge deployment with SageMaker Edge Manager.

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17
Q

Q: What are SageMaker Pipelines?

A

A: A tool for creating, automating, and managing machine learning workflows using pipelines.

18
Q

Q: What is AWS Data Wrangler?

A

A: A tool within SageMaker for preparing, cleaning, and transforming data for machine learning.

19
Q

Q: What is hyperparameter tuning?

A

A: The process of optimizing model parameters to improve performance using automated searches, such as SageMaker Automatic Model Tuning.

20
Q

Q: What are pre-trained models in AWS?

A

A: Models already trained on large datasets, provided by services like Amazon Rekognition, Comprehend, and Translate, for easy use without building custom models.

21
Q

Q: How does AWS support ML at the edge?

A

A: Using services like AWS IoT Greengrass and SageMaker Edge Manager to deploy models on edge devices.

22
Q

Q: What is AWS Inferentia?

A

A: A custom chip designed by AWS to accelerate machine learning inference workloads.

23
Q

Q: What is Explainable AI in SageMaker?

A

A: Tools like SageMaker Clarify provide insights into model predictions and detect biases in datasets.

24
Q

Q: What is SageMaker Model Monitor?

A

A: A tool for monitoring deployed models to detect data drift and performance issues.

25
Q

Q: What is transfer learning?

A

A: A technique where a pre-trained model is fine-tuned on a new dataset to improve performance in specific tasks.

26
Q

Q: What is SageMaker Neo?

A

A: A service that optimizes models for faster inference on various hardware platforms.

27
Q

Q: What are real-time predictions in AWS ML?

A

A: Predictions provided by a deployed model endpoint in response to live requests, supported in SageMaker.

28
Q

Q: What are batch predictions in AWS ML?

A

A: Predictions generated for large datasets without real-time interaction, processed in batches.

29
Q

Q: What machine learning frameworks does AWS support?

A

A: TensorFlow, PyTorch, Apache MXNet, Scikit-learn, XGBoost, and others.

30
Q

Q: What is the AWS Marketplace for Machine Learning?

A

A: A marketplace offering pre-trained models, algorithms, and datasets from third-party providers.

31
Q

Q: What is Amazon Forecast?

A

A: A service that uses machine learning to generate accurate time-series forecasts.

32
Q

Q: What is Amazon Personalize?

A

A: A service for building real-time personalized recommendations for applications.

33
Q

Q: What is reinforcement learning in AWS?

A

A: A type of ML where models learn by interacting with environments to maximize rewards, supported by AWS DeepRacer.

34
Q

Q: What is AWS DeepRacer?

A

A: An autonomous racing car platform for learning and experimenting with reinforcement learning.

35
Q

Q: Why is data labeling important for ML?

A

A: Properly labeled data ensures models learn accurately and improve prediction quality.

36
Q

Q: What are common use cases for AWS ML?

A

A: Fraud detection, recommendation engines, image recognition, text analysis, and predictive maintenance.

37
Q

Q: What are SageMaker Notebooks?

A

A: Jupyter-based environments in SageMaker for preparing data, building models, and visualizing results.

38
Q

Q: What are the three layers of the AWS AI/ML stack?

A
  • AI Services (e.g., Rekognition, Comprehend)
  • ML Services (e.g., SageMaker)
  • ML Frameworks and Infrastructure (e.g., Deep Learning AMIs, Inferentia)
39
Q

Q: How does SageMaker Clarify ensure model explainability?

A

A: By detecting biases in datasets and explaining model predictions with interpretable metrics.

40
Q

Q: What is responsible AI in AWS?

A

A: AWS’s approach to ensuring ethical, transparent, and fair machine learning practices using tools like SageMaker Clarify and robust monitoring solutions.

AWS (29 decks)

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