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Tag: Data Scientists

ML Needs Separate Dev and Ops Teams, Datatron Says

In the machine learning world, the folks developing models often are the same folks who are tasked with running the models in production. And they often use the same end-to-end ML software stacks. But emerging best pract Read more…

Why Data Scientists and ML Engineers Shouldn’t Worry About the Rise of AutoML

Low-code and no-code development tools are becoming increasingly popular, and the pandemic only accelerated this trend. When we think of low-code or no-code development, we’re usually referring to tools that allow a no Read more…

Why Data Science Is Still a Top Job

This year, for the first time since 2016, data scientist is not the number one job in America, according to Glassdoor’s annual ranking. While the shine may be wearing off the unicorn a bit, data science is still a grea Read more…

Is Kubernetes Really Necessary for Data Science?

It seems almost preordained at this point: Thou Shalt Run Thy Data Science Environment On a Cloud-Native Kubernetes Platform. This is 2020, after all. How else could it possibly run? But Tyler Whitehouse, a data scientis Read more…

Kepler AutoML Targets Next-Gen Business Analysts

As more companies roll out digital infrastructure, they are ingesting greater volumes of data that can be used by business analysts to gauge customer intent and boost transactions. Complexity and lack of data scientists Read more…

COVID-19 Gives AI a Reality Check

While it seems unlikely that AI will enter another nuclear winter, the current COVID-19 situation is giving enterprises the opportunity to rethink their AI strategies, giving the better AI projects more room to run, whil Read more…

2019: A Big Data Year in Review – Part One

At the beginning of the year, we set out 10 big data trends to watch in 2019. We correctly called some of what unfolded, including a renewed focus on data management and continued rise of Kubernetes (that wasn’t hard t Read more…

Dogged Determination: How Trupanion Pulled AI Across the Finish Line

David Jaw had reason to be excited. As a data scientist at Trupanion, Jaw had just put the finishing touches on the prototype of a machine learning model that could replicate the actions of a human claims adjuster with a Read more…

Restive Data Workers Head for Exits

In a sign of the growing bargaining power of “data workers,” one third responding to a recent survey said they would quit over what they considered an “unfair” performance review. Moreover, the survey released Read more…

Giving DevOps Teeth To Crunch Down on AI Ethics Governance

AI ethics is definitely trending. I’ve seen the phrase in my reading and heard it trip from the tongues of professional acquaintances many times in the past several months. Management fads come and go, and I wonder Read more…

‘Data Workers’ Failing to Cope

More evidence is emerging that “data workers” in general and data scientists in particular are bogged down by the sheer breadth of their company’s data. Meanwhile, the skills gap between data experts and line-of Read more…

BI Leader Sisense Acquires Periscope Data

Sisense’s acquisition of Periscope Data combines the buyer’s business intelligence platform with Periscope’s cloud data analytics expertise as Sisense looks to up its analytics game. The merger announced on Tues Read more…

AI Hype Rockets, Hadoop Twins, and Other Learnings from Strata

Strange similarities have been discovered to exist between Cloudera and Hortonworks now that the two former Hadoop rivals have merged into a single company. In fact, the resemblances are so great that they appear to be l Read more…

How To Find and Hire Data Scientists

So you're building a data science team. That's great news! As a business leader, finding a qualified data scientists is a critical step in your company's ability to harness big data and machine learning technologies, whi Read more…

The Hard Questions of Hiring For Machine Learning

I’ve been thinking a lot about hiring for the machine learning specialization lately. It’s no surprise. New data is emerging almost daily about the rise of machine learning, artificial intelligence and deep learning Read more…

Empowering Citizen Data Science

Companies of all stripes are turning to data science to unlock the value in their data. However, finding highly trained data scientists to build the systems has proven to be a very difficult task. Now many organizations Read more…

Anaconda: Data Science Exiting Hadoop for the Cloud

Data scientists are embracing cloud-native frameworks as they move on from on-premises data infrastructure previously dominated by Hadoop, concludes a survey on the state of data science. The shift is driven in part b Read more…

Opening Up Black Boxes with Explainable AI

One of the biggest challenges with deep learning is explaining to customers and regulators how the models get their answers. In many cases, we simply don't know how the models generated their answers, even if we're very Read more…

Why Developers Need to Think Like Data Scientists

Data is growing faster than is even fathomable. By 2020, roughly 1.7 megabytes of new information will be created every second for every human being on the planet. Given the immense amount of data, it’s no wonder there Read more…

What Kind of Data Scientist Are You?

If you've worked with the data science community, you've probably interacted with data scientists and formed a definition for the increasingly popular position. But it turns out, not all data scientists are alike, and ac Read more…

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