Remove Artificial Intelligence Remove Big Data Remove Data Engineering Remove Trends
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Five Trends for 2019

Hu's Place - HitachiVantara

Against this backdrop there are five trends for 2019 that I would like to call out. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared.

Trends 86
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Data engineers vs. data scientists

O'Reilly Media - Data

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.

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Data Innovation Summit with Gema Parreño – lead data scientist at Apiumhub

Apiumhub

Data Innovation Summit topics. Same as last year, the event offers six workshops (crash-course) themes, each dedicated to a unique domain area: Data-driven Strategy, Analytics & Visualisation, Machine Learning, IoT Analytics & Data Management, Data Management and Data Engineering.

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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. .

Data 105
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Top Data science books you should definitely read

Apiumhub

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by by EMC Education Services. The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

As a logical reaction to this problem, a new trend — MLOps — has emerged. It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development.

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AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

There is a lot more to machine learning in the enterprise than just the model, which is what many people think of when they hear artificial intelligence. These tasks are usually split over a data engineer, a data scientist, and a machine learning engineer. Adapted from Sculley et al.