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The insideBIGDATA IMPACT 50 List for Q2 2019

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Domino Data Lab Platform Enhancements Improve Productivity of Data Science Teams Across the Entire Model Lifecycle

Domino Data Lab , provider of a leading open data science platform, announced new capabilities to further empower model-driven organizations to institute data science as an enterprise-wide discipline.
Updated with three new breakthrough capabilities — Data sets, Experiment Manager, and Activity Feed — Domino helps data science teams accelerate development and delivery of high-impact models through increased collaboration, reproducibility, and reusability across their organizations.

Field Report: GPU Technology Conference 2019 #GTC19

I eagerly attended my 3rd GPU Technology Conference (GTC): “Deep Learning & AI Conference,” in Silicon Valley, March 23-26 as a guest of host NVIDIA. GTC has become my favorite tech event of the year due to its highly focused topic areas that align well with my own; data science, machine learning, AI, and deep learning; plus the show has an academic feel that I appreciate.

How Freelancing Offers a Solution for the AI and Data Science Talent Shortage

In this special guest feature, Pedro Alves Nogueira, Ph.D., Head of Artificial Intelligence and Data Science and a Director of Engineering at Toptal, observes that due to the low supply of AI professionals, competition to secure available talent is fierce. The hiring of AI specialists and data scientists is primarily monopolized by tech giants like Facebook and Google, which offer exorbitant salaries and competitive perks to AI talent — even those with little previous experience. This puts smaller companies that lack the resources to offer competitive incentives packages at a major disadvantage, and it continues to preclude them from finding talent to develop their technology.

Distributed GPU Performance for Deep Learning Training

If there is a time deadline by which training must be completed, or if it simply takes too long to complete training, distributing the workload across many GPUs can be used to reduce training time.  This flexibility allows GPU resources to be maximally utilized and provides high ROI since time to results can be minimized. HPE highlights recent research that explores the performance of GPUs in a scale-out and scale-up scenarios for deep learning training. 

High School Students Beat Trained Data Scientists at UC Berkeley, Solve Real Healthcare Problems with Aible AI in Minutes

Aible, the innovators of AI for business impact, announced the UC Berkeley Real World AI Challenge winners — the top scorers include two high school students, a history major and no data scientists. Nearly 30 high school and college students competed to create a custom Artificial Intelligence (AI) based on a real-world healthcare data set […]

insideBIGDATA 2019 Annual Executive Round Up

Our annual insideBIGDATA Executive Round Up showcases the insights of thought leaders on the state of the big data industry, and where it is headed. In our annual 2019 round up, we examine five topics: the importance of AI explainability in 2019, what industries are making the best competitive use of AI in 2019, how enterprises are seeking to improve technological infrastructure and cloud hosting processes for supporting AI, how AI-optimized hardware solves important compute and storage requirements, and how AI plays important roles at 3 leading companies.

Labeled Training Sets for Machine Learning

It’s no secret that machine learning success is derived from the availability of labeled data in the form of a training set and test set that are used by the learning algorithm. Labels are the values of the response variables (what’s being predicted) that are used by the algorithm along with the feature variables (predictors). One consistent problem faced by data scientists is how to obtain labels for a given data set for use with machine learning. In this article we’ll see a variety of techniques used down in the trenches.

TOP 10 insideBIGDATA Articles for February 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Infographic: The Typical Data Scientist 2019

It’s hardly a surprise to anyone in the tech and related industries that “data scientist” is the best job to have these days. After all, this has been what sources like the Harvard Business Review and Glassdoor report for what is now four years in a row. And even if we take the base salary of $108,000 out of the equation, the position is still plenty attractive on all other dimensions. The infographic below, produced by our friends over at 365DataScience, suggests that the field is evolving and, with it, the typical professional evolves as well.