DevOps.com

JANUARY 27, 2023

Data streams are all the rage. Once a niche element of data engineering, streaming data is the new normal—more than 80% of Fortune 100 companies have adopted Apache Kafka, the most common streaming platform, and every major cloud provider (AWS, Google Cloud Platform and Microsoft Azure) has launched its own streaming service.

Data 121

Data Google Cloud Azure AWS 121

TechCrunch

MAY 3, 2022

.” Galileo fits into the emerging practice of MLOps, which combines machine learning, DevOps and data engineering to deploy and maintain AI models in production environments. While investor interest in MLOps is on the rise, cash doesn’t necessarily translate to success.

Artificial Inteligence 223

Artificial Inteligence Machine Learning Development Software Review 223

Battery Ventures

OCTOBER 7, 2020

This has all translated into some prominent initial-public offerings for cloud-native companies this year—deals few could have imagined during the initial shock of the pandemic in March and April. Today, we delve deeper into these topics in our “State of the Cloud 2020” report.

Open Source 144

Open Source Cloud Google Cloud Azure 144

Altexsoft

JUNE 29, 2021

MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists. Who does what in a data science team. Machine learning engineers are relatively new to data-driven companies. Key components of an MLOps cycle.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Forbes notes that a full transition to the cloud has proved more challenging than anticipated and many companies will use hybrid cloud solutions to transition to the cloud at their own pace and at a lower risk and cost. This will be a blend of private and public hyperscale clouds like AWS, Azure, and Google Cloud Platform.

Cloud 78

Cloud Google Cloud Azure AWS 78

Apiumhub

AUGUST 12, 2021

This year you will have 6 unique tracks: Cloud Computing: IaaS, PaaS, SaaS DevOps: Microservices, Automation, ASRs Cybersecurity: Threats, Defenses, Tests Data Science: ML, AI, Big Data, Business Analytics Programming languages: C++, Python, Java, Javascript,Net Future & Inspire: Mobility, 5G data networks, Diversity, Blockchain, VR.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

ParkMyCloud

JANUARY 17, 2020

AWS Certified DevOps Engineer – Professional. Intended for individuals who have a DevOps engineer role and two or more years of experience operating, provisioning and managing AWS environments. Azure DevOps Engineer Expert. Azure Data Engineer Associate.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

O'Reilly Media - Ideas

MAY 1, 2019

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. Cloud Computing Governance , May 29.

Course 46

Course Training Artificial Inteligence Machine Learning 46

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

InfoQ Culture Methods

DECEMBER 28, 2020

As 2020 is coming to an end, we created this article listing some of the best posts published this year. This collection was hand-picked by nine InfoQ Editors recommending the greatest posts in their domain. It's a great piece to make sure you don't miss out on some of the InfoQ's best content.

Continuous Delivery 102

Continuous Delivery Google Cloud Data Engineering Microservices 102

O'Reilly Media - Ideas

JANUARY 25, 2024

Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6% Data engineering deals with the problem of storing data at scale and delivering that data to applications. Interest in data warehouses saw an 18% drop from 2022 to 2023.

Trends 114

Trends Technical Review Technology Artificial Inteligence 114

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

Altexsoft

JULY 19, 2022

This basic principle corresponds to that of agile software development or approaches such as DevOps, Domain-Driven Design, and Microservices: DevOps (development and operations) is a practice that aims at merging development, quality assurance, and operations (deployment and integration) into a single, continuous set of processes.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

JANUARY 14, 2021

What specialists and their expertise level are required to handle a data warehouse? However, all of the warehouse products available require some technical expertise to run, including data engineering and, in some cases, DevOps. Data loading. Data loading. Data loading. Integrations. Integrations.

Backup 115

Backup Azure Software Review Systems Review 115

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

Altexsoft

DECEMBER 15, 2021

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.

Machine Learning 59

Machine Learning Artificial Inteligence How To Azure 59

O'Reilly Media - Ideas

JANUARY 25, 2022

A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “data engineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” Operations, DevOps, and SRE. That decline continues.

Trends 109

Trends Technical Review Technology Artificial Inteligence 109

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

O'Reilly Media - Ideas

JANUARY 25, 2021

Operations or DevOps or SRE. There’s been a lot of discussion about operations culture (the movement frequently known as DevOps), continuous integration and deployment (CI/CD), and site reliability engineering (SRE). The term “DevOps” has fallen on hard times. Operations, DevOps, and SRE.

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145

UruIT

APRIL 27, 2020

Google Cloud . MathWork focused on the development of these tools to become experts in high-end financial use and data engineering contexts. It includes accessible tools to automate DevOps for ML, collaborate across various internal teams, and optimize hardware usage.

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 78

UruIT

JULY 23, 2019

Having these requirements in mind and based on our own experience developing ML applications, we want to share with you 10 interesting platforms for developing and deploying smart apps: Google Cloud. MathWork focused on the development of these tools in order to become experts on high-end financial use and data engineering contexts.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81