Deep Learning systems of Google, Amazon and Microsoft vs Pecan

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Automated AI Infrastructure

  • Pecan - Deep Learning
  • Google Cloud ML - Regressions, Trees and Forests Regressions, Deep Learning
  • MS Azure Brain - Regressions, Trees and Forests Regressions
  • AWS Sage Maker - Regressions, Trees and Forests Regressions


Data Processing

Uploading a file, a two-dimensional AI table ("Image") to which the AI algorithms can be applied

  • Pecan - The system uses a raw feed, and pulls existing tables into the database. Drag and drop functionality
  • Google Cloud ML - Data scientist prepares the table. The system assumes that there is a table prepared.
  • MS Azure Brain - Data scientist prepares the table. The system assumes that there is a table prepared.
  • AWS Sage Maker - Data scientist prepares the table. The system assumes that there is a table prepared.


Data Engineering

Mechanical Properties

  • Pecan - Is executed automatically, within minutes, using AI
  • Google Cloud ML - Unable to perform automatically
  • MS Azure Brain - Unable to perform automatically
  • AWS Sage Maker -  Unable to perform automatically


Model Construction

Selection of the algorithm(s) (Type - Regression) that runs in the table + configuration

  • Pecan - Full automation of the AI configuration and algorithms, including support for Deep Learning
  • Google Cloud ML - Deep Learning is only for images. The process always requires writing code
  • MS Azure Brain - The configuration and AI algorithms are automated, but not in a Deep Learning process. No need to write code. Required to manually build the flow chart of how the models run. Quick search of models and quick learning.
  • AWS Sage Maker -  The configuration and AI algorithms are automated, but not in a Deep Learning process. No need to write code. The function of model search is not developed


Testing and Validation

Receive a report from the system as to how accurate the algorithm was in the control group

  • Pecan - The system returns results that enable BI investigation. The AI system enables BI analytical investigations of predictions, and simulation of future business scenarios based on changes in an existing set. Business simulator in causal technology. See how the change of data effects the prediction
  • Google Cloud ML - The system returns standard metrics of system performance, an estimate of model accuracy (%), Integrated Counter factual capability
    System disadvantages:
    Does not return results that allow for BI investigation. Does not perform BI on the AI., Does not allow for analytical investigation of the predictions, or simulation of future business scenarios based on changes in an existing dataset.
  • MS Azure Brain - The system returns standard metrics of system performance, and an estimate of model accuracy (%),
    System disadvantages:
    Does not return results that allow for BI investigation. Does not perform BI on the AI. Does not allow for analytical investigation of the predictions, or simulation of future business scenarios based on changes in an existing data set. There is no business simulator in the causal technology, and you can't examine the effect of changing the data on the forecast.
  • AWS Sage Maker -  The system returns standard metrics of system performance, an estimate of model accuracy (%), System disadvantages: Does not return results that allow for BI investigation. Does not perform BI on the AI. Does not allow for analytical investigation of the predictions, or simulation of future business scenarios based on changes in an existing data set. There is no business simulator in the causal technology, and you can examine the effect of changing the data on the forecast.


Deployment

Embedded in Production

  • Pecan - Supports APIs and other more convenient forms that do not involve code. Continuous updating of the database (continuous DB).
  • Google Cloud ML - APIs that insert table rows into it and return model outputs.
    The system assumes that there is a table of the AI  ready (Panel).
  • MS Azure Brain - APIs that insert table rows into it and return model outputs.
    The system assumes that there is a table of the AI  ready (Panel).
  • AWS Sage Maker -  APIs that insert table rows into it and return model outputs.
    The system assumes that there is a table of the AI  ready (Panel).


Knowledge required to train the model

  • Pecan -
  • Google Cloud ML - Data Scientist or Data Researcher
  • MS Azure Brain - Data Scientist or Data Researcher
  • AWS Sage Maker -  Data Scientist ¹ or Data Researcher


Knowledge required to activate the model and ask questions

  • Pecan - Data Analyst or BI Analyst
  • Google Cloud ML - Data Scientist or Data Researcher
  • MS Azure Brain - Data Scientist or Data Researcher
  • AWS Sage Maker -  Data Scientist or Data Researcher


¹ A data scientist is a person who has the knowledge and skills to conduct sophisticated and systematic analyses of data. The data scientist extracts insights from data sets for product development, and evaluates and identifies strategic opportunities.

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