O'Reilly Media - Data

APRIL 11, 2018

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

Data Engineering 280

Data Engineering Engineering Data Artificial Inteligence 280

TechCrunch

DECEMBER 10, 2020

” It currently has a database of some 180,000 engineers covering around 100 or so engineering skills, including React, Node, Python, Agular, Swift, Android, Java, Rails, Golang, PHP, Vue, DevOps, machine learning, data engineering and more. It starts with an AI platform to source and vet candidates.

Engineering 325

Engineering Artificial Inteligence Lambda Machine Learning 325

OpenCredo

JULY 31, 2018

Machine Learning, alongside a mature Data Science, will help to bring IT and business closer together. By leveraging data for actionable insights, IT will increasingly drive business value. Agile and DevOps practices enable the continuous delivery of business value through productionised machine learning models and software delivery.

Artificial Inteligence 40

Artificial Inteligence Machine Learning Data Artificial Intelligence 40

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Hybrid clouds must bond together the two clouds through fundamental technology, which will enable the transfer of data and applications. Data scientists, DevOps engineers, big data consultants, cloud architects, AppDev engineers, and many more – all of them smart and collaborative.

Cloud 78

Cloud Google Cloud Azure AWS 78

Xebia

FEBRUARY 25, 2024

DevOps may sound familiar, but nowadays there are a lot more terms: LLMOps, LegOps (no, not Lego-Ops), and of course MLOps. Data science is generally not operationalized Consider a data flow from a machine or process, all the way to an end-user. Machine learning operations: what and why MLOps, what the fuzz?

Applications 130

Applications Technical Review Software Review Azure 130

O'Reilly Media - Ideas

JULY 28, 2020

For example, an AI product that helps a clothing manufacturer understand which materials to buy will become stale as fashions change. Many mature DevOps processes and tools, honed over years of successful software product releases, make these processes more manageable, but they were developed for traditional software products.

Marketing 145

Marketing Weak Development Team Metrics UI/UX 145

BPM

JANUARY 18, 2019

Successful organizations will differentiate themselves by ensuring the customer experience is not a fashion or an afterthought, but instead lies at the very heart of how they organize and run their business. AI-enabled data engines will provide insight about what processes can be redesigned and/or automated.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111