Monument offers useful predictions with no-code at all!

A COVID active cases and deaths model for New Jersey, built in Monument.

Step 1: What we want to test

The specific questions we’re answering here are:

Quickly build production-ready Machine Learning workflows with model serving.

“Let’s not lose our cool — then we’re no better than the machine!” (Copyright Paramount Pictures.)

Two enter, one remains…(Source: Wikipedia)

Take it easy.

1. It’s easier to get started.

Data science does not have to mean months or years of work to get started. With a no-code platform you can go from literally zero coding or data science experience to running your first algorithm in minutes — literally!

2. It’s easier to visualize your results.

It’s hard to find the meaning in arrays of numbers, and visualizing your data is its own coding hurdle. No-code tools automatically visualize your results so that you can see the results of your work.

3. It’s easier to experiment with different methods.

Regressions, Neural Networks, Kalman Filters. If you’re learning to code with, for example, Python, you’re going to deal with an entirely new learning curve for each…

Apply a classification algorithm in seconds!

“Credit Cards and Cash” by Sean MacEntee is licensed under CC BY 2.0

Obtaining & Inspecting The Data

In the “Data Folder” of the University of California-Irvine repository linked above, there is a file called default of credit card clients.xls. When we open it up in a spreadsheet, it looks like this:

The NOAA Ocean Data in this Tutorial Covers Florida & the Gulf of Mexico.


Predictions to keep you two steps ahead.

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