Path3.Mod1.a - Automated Machine Learning - What is it? Flashcards

1
Q

AutomatedML defined

A

The process of automating the time-consuming, iterative tasks of machine learning model development. It allows us to try different model training algorithms with a variety of hyperparameters in parallel in order to find the one that best fits your data

See What is AutoML

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2
Q

NoCo Save BP Ag OF

AutomatedML Advantages

A
  • Implementing ML solutions without extensive programming knowledge
  • Saves time and resources
  • Leverage Data Science Best Practices
  • Provides Agile problem-solving
  • Provides charts and metrics to help identify risks related to overfitting and helps to mitigate them
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3
Q

Id CoNoCo DS CMLP Job Rev

Six steps for designing and running an AutoML experiment

A
  1. Identify the type of ML Problem: Classification, Time-Series Forecasting, Regression, Computer Vision or NLP?
  2. Choose Code-First Experience (SDKs) or No-Code Studio Web Experience (ML Studio)
  3. Specify the source of your labeled training data
  4. Configure ML parameters; # of iterations, what models to try, hyperparameter tuning, advanced preprocessing/featurization, and selecting metrics
  5. Submit the training job
  6. Review the results in the logged job info (metrics)
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4
Q

C R TSF CV NLP

The five ML Workloads supported by AutoML
(you know this from AI-900)

A
  • Classification: Predict which categories new data will fall into, based on learnings from its training data.
  • Regression: Predict numeric values based on independent predictors, in order to establish a relationship between those predictors, by estimating how each variable impacts others.
  • Time-Series Forecasting: A multivariant regression problem where past values are pivoted to become additional dimensions for the regressor with other predictors. It naturally incorporates multiple contextual variables and their relationship to one another.
  • Computer Vision: Generate models trained on image data for Vision-related scenarios
  • Natural Language Processing: Generate models trained on text for classification and named entity
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5
Q

Two options for Code-First AzureM ML experiences

A
  1. Azure ML Python SDKv2
  2. Azure ML CLIv2 (REST APIs)
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6
Q

MCC MLC OD IS

Four AutoML-supported Vision tasks

A
  • Multi-class image Classification: Images are classified with a single label from a set of possible class/categorization values
  • Multi-label image Classification: Images are classified with one or more labels from a set of possible class/categorization values
  • Object Detection: Objects are identified and located using bounding boxes within an image
  • Instance Segmentation: Identify object at the pixel level, drawing polygons around each object in the image
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7
Q

MCTC MLTC NER

Three AutoML-supported NLP tasks and what file types work best with each

A
  • Multi-Class Text Classification: multiple categories, each data sample can only be classified as exactly ONE class (find the best match). .CSV works for this task
  • Multi-Label Text Classification: multiple categories, each data sample can be classsifeid as MORE THAN ONE class (find ALL that apply). .CSV works for this task
  • Named Entity Recognition: multiple possible tags for tokens in sequence. The task at hand is to predict tags for tokens for each sequence. Requires .TXT files that use a space as the separator and adhere to the CoNLL format (Conference on Natural Language Learning)
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