Jack Riewe

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.

Modern Product Managers are in charge of sparking innovation in their company, but few know how to change data science into user science.

Enlist the help of data to improve creative messaging and enhance customer experience.

Customers churning is just a part of business, but what if—with machine learning—you can predict a customer leaving?

Here are some examples of how to use data to apply dynamic pricing for your business.

Let’s talk about the steps it takes to become an AI collaborator, like Toro y Moi.

Let’s define AI, machine learning, and predictive analytics in an easy, non-technical way.

While data can make everyone an oracle, not everyone knows the capability of creative AI.

We discuss our process behind creating this blog and the topics that we will write about.