Emerging Data Center Trends: From DevOps to DataOps
DevOps transformed software delivery, but data-intensive applications exposed its limits. DataOps is emerging as the next paradigm—here’s what it means for enterprise data centers.
How Containers and Kubernetes Change the Rules for Doing Agile at Scale
Linux containers and Kubernetes are closing the gap between development and operations, making it practical for large enterprises to adopt DevOps and agile at scale. Here is what that means in practice.
IT ops pros predict routes to DevOps efficiency in 2018
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How to Navigate the Four Stages of a DevOps Transformation
DevOps adoption doesn’t happen overnight. This guide breaks down the four key stages of a DevOps transformation—from manual, batch-based releases to enterprise-wide continuous delivery—and explains what teams should focus on at each stage.
5 reasons DevOps will be a big deal in the year ahead
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DevOps Automation: Why It Was Always About DevOps
From Barry Boehm’s 1986 Spiral Model to today’s closed-loop CI/CD pipelines, the drive toward faster, automated software delivery has always pointed toward DevOps. Here’s why the underlying principles matter more than ever.
What is DataOps? Collaborative, Cross-Functional Analytics Explained
DataOps brings together DevOps, data engineering, and data science into a unified discipline. Learn what DataOps is, its core principles, where it fits in modern enterprises, and how to build an effective DataOps team.
Box Introduces Box Skills: A Framework for Applying Machine Learning to Cloud Content
At BoxWorks 2017, Box unveiled Box Skills, a framework that brings machine learning capabilities—including audio transcription, video intelligence, and image recognition—to cloud-stored content, alongside the Box Skills Kit for custom implementations.
Docker, Ansible, and Kubernetes: The Top 3 Tools DevOps Engineers Should Learn
According to Packt’s 2017 Skill Up survey of 5,000 tech professionals, DevOps engineers ranked Docker, Ansible, and Kubernetes as the three most important tools to learn. Here’s what the data says about salaries, skills, and career priorities.
7 Machine Learning Tools for IIoT: Open-Source Platforms for Industrial Use
Explore seven open-source and cloud-based machine learning tools—including TensorFlow, H2O.ai, Apache MLlib, and Caffe—that industrial and enterprise teams can use to integrate ML into IIoT workflows.
4 Things Machine-Learning Algorithms Can Do for DevOps
Machine learning is no longer a future concept for DevOps teams — it’s a practical tool for smarter alerting, root-cause analysis, and automated recommendations. Here are four concrete areas where ML algorithms add real value.
4 Key Considerations for Deploying a Machine Learning System in Production
Moving a machine learning model from a notebook to a production system is harder than building the model itself. Here are four critical considerations—plus how DevOps practices can close the gap.