The machine learning problem of the next decade

We're used to computers being consistent and reliable. But as we build more and more complicated machine learning systems to do more and more things that people used to do, they become less consistent and less reliable. Businesses...

01/21/2016

How machine learning will affect your business

In the past, successful use of machine learning algorithms required bespoke algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Amazon and Alibaba all launched turnkey cloud based machine learning...

11/20/2015

Why human-in-the-loop computing is the future of machine learning

Artificial Intelligence is here and it’s changing every aspect of how business functions. But it’s not replacing people one job function at a time. It’s making people in every job function more efficient by handling the easy cases and...

11/13/2015

The simple way to make data science effective

Data scientists are trained to make more efficient algorithms, but the simple way to make an algorithm work better is to feed it more data.

08/04/2015

It's time for data science to be part of your hiring process

Nearly every CEO will tell you human talent is the reason their business is successful: A great sales hire can change the direction of your entire company, while a bad engineering hire could result in your product falling flat on its...

07/20/2015

How to hire data engineers

In order to build a great data science practice, you need great data engineers. Here's how to hire them.

06/11/2015

You're hiring the wrong data scientists

Companies are building data scientist teams which is great. But they are not giving them the support they need and they're incurring a ton of unnecessary overhead.

06/05/2015

The data science ecosystem, part 3: Data applications

The data science ecosystem, part 3: Data applications

The third part in a series on the data science ecosystem looks at the applications that turn data into insights or models.

04/10/2015

The data science ecosystem part 2: Data wrangling

The data science ecosystem part 2: Data wrangling

Data scientists spend 80% of their time convert data into a usable form. There are many tools out there to help and I will go over some of the most interesting.

04/01/2015

The data science ecosystem

The data science ecosystem

Data science isn't new, but the demand for quality data has exploded recently. This isn't a fad or a rebranding, it's an evolution.

03/24/2015

We need open data to become the new open source

Data is becoming more and more critical to businesses, but almost all data is siloed inside corporations. The lack of open data sets today holds innovation back and that needs to change.

03/16/2015

Why more data isn't always better

In the past 10 years, the focus of data has been on amassing and storing: the more data collected, the better. But while we all became expert data gatherers, what we actually ended up with was a glut of data, a shred of the insights...

02/23/2015

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