Altexsoft

JULY 23, 2020

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.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

CIO

APRIL 27, 2023

The demand for specialized skills has boosted salaries in cybersecurity, data, engineering, development, and program management. DevOps engineer DevOps engineers are tasked with managing IT infrastructure, identifying requirements, overseeing software testing, and monitoring performance of software and services after they are deployed.

Technical Review 174

Technical Review Software Review Systems Review Software Engineering 174

Confluent

FEBRUARY 6, 2019

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.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Xebia

FEBRUARY 25, 2024

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.

Applications 130

Applications Technical Review Software Review Open Source 130

CircleCI

JULY 29, 2022

In the CLI version, you have full control of your data project configuration and the ability to publish documentation as needed, while dbt Cloud provides a user interface that sets up a few configurations for you and generates dbt documentation automatically. Why is dbt useful in data engineering and analysis?

Testing 98

Testing Software Review Continuous Integration Software Development 98

Github

OCTOBER 23, 2019

Our help site runs on a continuous integration system with Crowdin , a localization tool and one of our GitHub Marketplace partners. Continuous integration allows us to always publish the latest articles in Portuguese or any other GitHub-supported language.

Artificial Inteligence 15

Artificial Inteligence Machine Learning Continuous Integration Open Source 15

Tenable

NOVEMBER 3, 2022

When our data engineering team was enlisted to work on Tenable One, we knew we needed a strong partner. When Tenable’s product engineering team came to us in data engineering asking how we could build a data platform to power the product, we knew we had an incredible opportunity to modernize our data stack.

Data 98

Data AWS Storage Data Engineering 98

Cloudera

OCTOBER 7, 2022

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD). Introduction.

SDLC 81

SDLC Data Open Source Handbook 81

Apiumhub

NOVEMBER 17, 2022

Clare Sudbery – Independent Technical Coach specialized in TDD, refactoring, continuous integration, and other eXtreme Programming (XP) practices. Jesse Anderson – Data Engineer, Creative Engineer, and Managing Director of Big Data Institute.

Technical Review 80

Technical Review Software Review Fractional CTO CTO Coach 80

Altexsoft

JANUARY 26, 2022

In recent years, it’s getting more common to see organizations looking for a mysterious analytics engineer. As you may guess from the name, this role sits somewhere in the middle of a data analyst and data engineer, but it’s really neither one nor the other. Here’s the video explaining how data engineers work.

Analytics 59

Analytics Engineering Data Engineering Software Engineering 59

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

O'Reilly Media - Ideas

JUNE 19, 2019

Other noteworthy items include: Tools for continuous integration and continuous testing of models. Discussions around machine learning tend to revolve around the work of data scientists and model building experts. A model is not “correct” if it returns a valid value—it has to meet an accuracy bar.

Government 103

Government Artificial Inteligence Machine Learning Testing 103

Netflix Tech

FEBRUARY 9, 2022

Let’s define some requirements that we are interested in delivering to the Netflix data engineers or anyone who would like to schedule a workflow with some external assets in it. In a typical Dataflow configuration this manual testing is optional because Dataflow continuous integration tests will do that for us on any pull-request.

Data 84

Data Testing Software Review Systems Review 84

ParkMyCloud

MAY 29, 2019

As more and more enterprises drive value from container platforms, infrastructure-as-code solutions, software-defined networking, storage, continuous integration/delivery, and AI, they need people and skills on board with ever more niche expertise and deep technological understanding.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

Progress

DECEMBER 30, 2019

AWS Amplify is a set of libraries, UI components, and a command line interface to build a mobile backend and integrate with your mobile and web apps. AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers.

AWS 52

AWS DevOps Disaster Recovery Serverless 52

Progress

DECEMBER 30, 2019

AWS Amplify is a set of libraries, UI components, and a command line interface to build a mobile backend and integrate with your mobile and web apps. AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers.

AWS 52

AWS DevOps Disaster Recovery Serverless 52

Progress

DECEMBER 30, 2019

AWS Amplify is a set of libraries, UI components, and a command line interface to build a mobile backend and integrate with your mobile and web apps. AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers.

AWS 10

AWS DevOps Disaster Recovery Serverless 10

Invid Group

AUGUST 28, 2024

Consider tools like CicleCI [22] for Continuous Integration (CI) and Continuous Delivery (CD) to speed up testing new changes and their deployment to production. They come in all flavors: different formats, templates, and from different legal processes, sizes, and quality.

Technical Review 52

Technical Review Artificial Inteligence Performance Azure 52

O'Reilly Media - Ideas

MARCH 1, 2023

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.

Trends 144

Trends Technical Review Technology Software Review 144

DataRobot

FEBRUARY 9, 2022

Solution: Because MLOps allows model reuse, data scientists do not have to create the same models over and over, and the business can package, control, and scale them. Most organizations find that the best MLOps solution is an external system that provides a single environment for continuous integration and deployment of AI projects. .

Artificial Inteligence 52

Artificial Inteligence Machine Learning eBook Training 52

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. Getting started with continuous integration , June 20.

Course 62

Course Training Artificial Inteligence Machine Learning 62

Kentik

FEBRUARY 7, 2023

IT personnel structure will need to undergo a corresponding shift as service models change, needed cloud competencies proliferate, and teams start to leverage strategies like continuous integration and continuous delivery/deployment (CI/CD). These adaptations can be expensive at the onset.

Network 59

Network Cloud Data Data Center 59

Altexsoft

SEPTEMBER 28, 2021

Particularly, it facilitates the work of researchers, data scientists, data engineers , QA engineers , and DevOps specialists. Buildbot for continuous integration (CI). Versatility plus extensive toolset for almost everything. Python can be applied to a wide range of tasks beyond software development.

Weak Development Team 94

Weak Development Team Programming Software Review Systems Review 94

Altexsoft

DECEMBER 14, 2022

delivering microservice-based and cloud-native applications; standardized continuous integration and delivery ( CI/CD ) processes for applications; isolation of multiple parallel applications on a host system; faster application development; software migration; and. Typical areas of application of Docker are.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

CIO

AUGUST 3, 2022

Modern delivery is product (rather than project) management , agile development, small cross-functional teams that co-create , and continuous integration and delivery all with a new financial model that funds “value” not “projects.”. If moving software from a supporting to a starring role is the what, then modern delivery is the how.

Leadership 218

Leadership IoT Artificial Inteligence Machine Learning 218

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.”

Trends 116

Trends Technical Review Technology Artificial Inteligence 116

O'Reilly Media - Ideas

JANUARY 25, 2021

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). Cloud computing has replaced data centers, colocation facilities, and in-house machine rooms.

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145

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.

Artificial Inteligence 59

Artificial Inteligence Machine Learning How To Open Source 59