What does Autonomous Mode Mean?

knowledge autonomus1Autonomous Mode is a feature built into Logic Plum’s automated machine learning platform that allows users – even those with no programming background – to build, deploy, and maintain machine learning models for your organization.

Why is Autonomous Mode Extraordinary?

Autonomous Mode enables analytics users of all skill levels – not just the highly-skilled data scientists – to leverage the infinite power of machine learning. By incorporating data science and analytics best practices, users produce accurate and efficient models.

When you identify which features have the most significant impact on results, you make better decisions. Autonomous enables this and can also predict customer outcomes, which is key to prescribing the best action for your business to take next.

These models harness data and produce insights that allow users to uncover opportunities and make predictions that can provide unlimited momentum for your business.

By automating all of the steps in the modeling process and incorporating built-in guardrails that ensure no actions are forgotten, Autonomous makes AI accessible to more people throughout your organization.

 

Who Can Benefit from Autonomous Mode?

It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage autonomous mode or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from autonomous mode.

 

Autonomous Mode and LogicPlum

LogicPlum’s Autonomous Mode is easy to use.

The user simply uploads the dataset, chooses a variable, and clicks the start button. LogicPlum’s Autonomous then gets to work:

  • LogicPlum calculates the correlation between each feature and the target.
  • LogicPlum then automatically performs the prep-work usually required by a data scientist before building the actual model, including:
  • designing a holdout set
  • specifying the appropriate validation approach
  • and selecting accuracy metric

By automating these processes, LogicPlum saves the user time, ensures best practices are followed, and puts up “guardrails” that prevent less sophisticated users from skipping steps or making mistakes.

Next, LogicPlum accesses an extensive database of model blueprints, which combine machine learning algorithms and various preprocessing engineering steps, to select the most appropriate plan for solving the problem at hand.

By leveraging such an extensive database, LogicPlum casts the broadest net possible to exhaustively test the problem in a variety of ways to produce the optimal solution.

After LogicPlum builds the models, it ranks them by accuracy and offers various support features that help explain and interpret the results.

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