Going forward, we want our users to treat any of their data as queryable, so look at machine learning like this: Google for Enterprise Data.
Being data-independent promotes collaboration, speed, and creativity with analyzing and making predictions.
There is a skill that overshadows technical machine learning skills in solving problems, integrating data into your company’s workflow, and using data to its fullest potential.
So you’ve added ML into your workflow, now what? How do you analyze and get value out of the data you collected? You have to be data literate.
Before you deploy your bike share fleet, follow this no-code ML process.
Instead of talking to data in a SQL query, it’s much more manageable and less time consuming to ask your data questions another way.
With collaborative machine learning, there is an increase in the transparency of your data predictions, avoiding bias and unfair outputs.
Run complex Predictions and Analytics on your data simply by asking questions in plain English.