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Development

Featured in Development

Beyond the Breach: Proactive Defense in the Age of Advanced Threats

Michael Brunton-Spall discusses some of the most advanced attacks that are in the public domain, mostly attributed in public by commercial organizations.

All in development

Architecture & Design

Featured in Architecture & Design

Evolving Your Architecture: Essential Steps and Tools for Modernization

Camilla Crispim discusses aspects of architecture that require assessment, methods for conducting these assessments, and the tools available to facilitate the process and support decision-making.

All in architecture-design

AI, ML & Data Engineering

Featured in AI, ML & Data Engineering

Trends in InfoSec: Data Minimization, Autoclassification, and Ethical AI

Rachael Greaves provides a summary of the requirements for data lifecycle management, the technology approaches, and the risks, and includes a Data Minimization Best Practice Checklist.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Reaching Your Automatic Testing Goals by Enhancing Your Test Architecture

If you have automatic end-to-end tests, you have test architecture, even if you’ve never given it a thought. Test architecture encompasses everything from code to more theoretical concerns like enterprise architecture, but with concrete, immediate consequences. Let's explore how you can achieve the goals you have for your automatic testing effort.

All in culture-methods

DevOps

Featured in DevOps

From Local to Production: a Modern Developer’s Journey towards Kubernetes

Urvashi Mohnani discusses the full developer experience of writing an application, containerizing it locally, deploying it to a Kubernetes cluster, and debugging Kubernetes applications locally.

All in devops

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AI, ML & Data Engineering

AutoML: the Promise vs. Reality According to Practitioners

Automation to improve machine learning projects comes from a noble goal, but true end-to-end automation is not available yet. As a collection of tools, AutoML capabilities have proven value but need to be vetted more thoroughly. Findings from a qualitative study of AutoML users suggest the future of automation for ML and AI rests in the ability for us to realize the potential of AutoMLOps.

Doris Xin
on Aug 29, 2022
AI, ML & Data Engineering

State of the Art in Automated Machine Learning

InfoQ caught up with experts Francesca Lazzeri, machine learning scientist lead at Microsoft; Matthew Tovbin, co-founder of Faros AI; Adrian de Wynter, applied scientist in Alexa AI’s Secure AI Foundations; Leah McGuire, principal member of technical staff at Salesforce; and Marios Michailidis, data scientist at H2O.ai, about the state of the art in automated machine learning (AutoML).

Anthony Alford Francesca Lazzeri Matthew Tovbin Adrian de Wynter Leah McGuire Marios Michailidis
on Aug 05, 2020

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