2016 Artificial Inteligence Data Engineering 
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.
AWS Machine Learning - AI
JUNE 18, 2024
More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. The fundamental objective is to build a manufacturer-agnostic database, leveraging generative AI’s ability to standardize sensor outputs, synchronize data, and facilitate precise corrections.
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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.
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.
Mobilunity
NOVEMBER 11, 2024
We already have our personalized virtual assistants generating human-like texts, understanding the context, extracting necessary data, and interacting as naturally as humans. It’s all possible thanks to LLM engineers – people, responsible for building the next generation of smart systems. What’s there for your business?
Altexsoft
JUNE 6, 2019
Fast checkout, personalized recommendations, or instant access to customer care at any time are a few services that can be implemented with the help of artificial intelligence. In December 2016, Amazon introduced the ‘Just Walk Out’ shopping experience with the first Amazon Go store in its Seattle office building.
O'Reilly Media - Data
MARCH 22, 2018
A deep dive into model interpretation as a theoretical concept and a high-level overview of Skater. 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.
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