2016 Data Engineering Machine Learning 
TechCrunch
OCTOBER 14, 2020
Ghodsi took over as CEO in 2016 after serving as the company’s VP of engineering. Ghodsi reckons you need three things: First, data engineering, or getting customer data “massaged into the right forms so that you can actually start using it.” He’s also a co-founder.
d2iq
FEBRUARY 19, 2021
Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.
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TechCrunch
APRIL 11, 2023
Krupenya says this capability puts data administration in reach of not just the most technical data engineers, but also people in other lines of business roles, who normally might not have access to tools like this. “So So actually anyone who needs to work with data can use DBeaver,” she told TechCrunch.
O'Reilly Media - Data
MARCH 22, 2018
Over the years, machine learning (ML) has come a long way, from its existence as experimental research in a purely academic setting to wide industry adoption as a means for automating solutions to real-world problems. 2016, DeepFool and Goodfellow, et al., 2016, DeepFool and Goodfellow, et al.,
Hu's Place - HitachiVantara
MAY 10, 2019
Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.
Hu's Place - HitachiVantara
JANUARY 3, 2019
Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Building an AI or machine learning model is not a one-time effort.
DataRobot
MAY 11, 2021
It 10x’s our world-class AI platform by dramatically increasing the flexibility of DataRobot for data scientists who love to code and share their expertise across teams of all skill levels. At DataRobot, we have always known that data science is a team sport. Data Exploration, Visualization, and First-Class Integration.
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