Remove Continuous Integration Remove Data Engineering Remove Google Cloud
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Foote Partners: bonus disparities reveal tech skills most in demand in Q3

CIO

An average premium of 12% was on offer for PMI Program Management Professional (PgMP), up 20%, and for GIAC Certified Forensics Analyst (GCFA), InfoSys Security Engineering Professional (ISSEP/CISSP), and Okta Certified Developer, all up 9.1% since March.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

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. MLOps lies at the confluence of ML, data engineering, and DevOps.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. integration) and preprocessing need to run at scale.

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Demystifying MLOps: From Notebook to ML Application

Xebia

Data science is generally not operationalized Consider a data flow from a machine or process, all the way to an end-user. 2 In general, the flow of data from machine to the data engineer (1) is well operationalized. You could argue the same about the data engineering step (2) , although this differs per company.

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160+ live online training courses opened for May and June

O'Reilly Media - Ideas

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. Getting started with continuous integration , June 20.

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AutoML: How to Automate Machine Learning With Google Vertex AI, Amazon SageMaker, H20.ai, and Other Providers

Altexsoft

The rest is done by data engineers, data scientists , machine learning engineers , and other high-trained (and high-paid) specialists. Also called DevOps for machine learning, MLOps is a mix of philosophy and practices that facilitates mutual understanding between a data science team and operations specialists.

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Technology Trends for 2023

O'Reilly Media - Ideas

Data Data is another very broad category, encompassing everything from traditional business analytics to artificial intelligence. Data engineering was the dominant topic by far, growing 35% year over year. Data engineering deals with the problem of storing data at scale and delivering that data to applications.

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