Full Stack Deep Learning
  • Full Stack Deep Learning
  • Course Content
    • Setting up Machine Learning Projects
      • Overview
      • Lifecycle
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    • Infrastructure and Tooling
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      • Resource Management
      • Frameworks and Distributed Training
      • Experiment Management
      • Hyperparameter Tuning
      • All-in-one Solutions
    • Data Management
      • Overview
      • Sources
      • Labeling
      • Storage
      • Versioning
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    • Machine Learning Teams
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    • Training and Debugging
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      • Start Simple
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      • Project Structure
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    • Research Areas
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    • Where to go next
  • Guest Lectures
    • Xavier Amatriain (Curai)
    • Chip Huyen (Snorkel)
    • Lukas Biewald (Weights & Biases)
    • Jeremy Howard (Fast.ai)
    • Richard Socher (Salesforce)
    • Raquel Urtasun (Uber ATG)
    • Yangqing Jia (Alibaba)
    • Andrej Karpathy (Tesla)
    • Jai Ranganathan (KeepTruckin)
    • Franziska Bell (Toyota Research)
  • Corporate Training and Certification
    • Corporate Training
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  1. Course Content
  2. Data Management

Storage

What are appropriate ways to store your data?

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Last updated 5 years ago

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Summary

  • Data storage requirements for AI vary widely according to the application and the source material.

  • The filesystem is the foundational layer of storage. Its fundamental unit is a “file” — which can be text or binary, is not versioned, and is easily overwritten.

  • Object storage is an API over the filesystem that allows users to use a command on files (GET, PUT, DELETE) to a service, without worrying where they are actually stored. Its fundamental unit is an “object” — which is usually binary (images, sound files…).

  • The database is a persistent, fast, and scalable storage/retrieval of structured data. Its fundamental unit is a “row” (unique IDs, references to other rows, values in columns).

  • A data lake is the unstructured aggregation of data from multiple sources (databases, logs, expensive data transformations). It operates under the concept of “schema-on-read” by dumping everything in and then transforming the data for specific needs later.

Storage - Data Management