InfoQ Homepage Machine Learning Content on InfoQ
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Data Science Community Reacts to COVID-19 Pandemic
The data science community has reacted with fervor to the COVID-19 pandemic, with numerous articles from a data-oriented perspective and both official and grassroot efforts to provide access to data and utilize ML techniques to help deal with the crises across industry, academia and governmental organizations worldwide.
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Google Announces Cloud AI Platform Pipelines to Simplify Machine Learning Development
In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility.
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Google's MediaPipe Machine Learning Framework Web-Enabled with WebAssembly
Google recently presented MediaPipe graphs for browsers, enabled by WebAssembly and accelerated by the XNNPack ML Inference Library. As previously demonstrated on mobile (Android, iOS), MediaPipe graphs allow developers to build and run machine-learning (ML) pipelines, to achieve complex tasks.
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JavaScript Face Detection with face-api.js
The face-api.js JavaScript module implements convolutional neural networks to solve for face detection and recognition of faces and face landmarks. The face-api.js leverages TensorFlow.js and is optimized for the desktop and mobile web.
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TensorFlow Quantum Joins Quantum Computing and Machine Learning
TensorFlow Quantum (TFQ) brings Google quantum computing framework Cirq and TensorFlow together to enable the creation of quantum machine learning (ML) models.
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MIT CSAIL TextFooler Framework Tricks Leading NLP Systems
A team of researchers at the MIT Computer Science & Artificial Intelligence Lab (CSAIL) recently released a framework called TextFooler which successfully tricked state-of-the-art NLP models (such as BERT) into making incorrect predictions.
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OpsRamp Introduces AI-Driven Suggestions for Incident Remediation
OpsRamp, a SaaS platform for hybrid infrastructure discovery, monitoring, management and automation has launched OpsQ Recommend Mode, a capability for incident remediation. OpsQ Recommend Mode provides predictive analytics to digital operations teams with the goal of reducing Mean Time to Resolution (MTTR).
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Boosting Apache Spark with GPUs and the RAPIDS Library
At the 2019 Spark AI Summit Europe conference, NVIDIA software engineers Thomas Graves and Miguel Martinez hosted a session on Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS Library. InfoQ recently talked with Jim Scott, head of developer relations at NVIDIA, to learn more about accelerating Apache Spark with GPUs and the RAPIDS library.
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GitHub Releases ML-Based "Good First Issues" Recommendations
GitHub shipped an updated version of good first issues feature which uses a combination of both a machine learning (ML) model that identifies easy issues, and a hand curated list of issues that have been labeled "easy" by project maintainers. New and seasoned open source contributors can use this feature to find and tackle easy issues in a project.
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Algorithmia Adds GitHub Integration to Machine Learning Platform
Algorithmia, an AI model management automation platform for data scientists and machine learning (ML) engineers, now integrates with GitHub.
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Elastic Stack 7.6 Released with Security, Performance, and Observability Improvements
Elastic announced the release of Elastic Stack 7.6. This release contains a number of security improvements including a new SIEM detection engine and a redesigned SIEM overview dashboard page. This release also includes performance improvements to queries that are sorted by date, enhanced supervised machine learning capabilities, and support for ingesting Jaeger trace data.
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Jenkins Creator Launches ML Startup in Continuous Risk-Based Testing
Jenkins creator, Kohsuke Kawaguchi, starts Launchable, a startup using machine learning to identify risk-based tests. Testing thought leader Wayne Ariola also writes about the need for a continuous testing approach, where targeted risk-based tests help provide confidence for continuous delivery.
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Stanford Researchers Publish AI Index 2019 Report
The Stanford Human-Centered Artificial Intelligence Institute published its AI Index 2019 Report. The 2019 report tracks three times the number of datasets as the previous year's report, and contains nearly 300 pages of data and graphs related to several aspects of AI, including research, technical performance, education, and societal considerations.
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Deep Java Library: New Deep Learning Toolkit for Java Developers
Amazon released Deep Java Library (DJL), an open-source library with Java APIs to simplify training, testing, deploying, and making predictions with deep-learning models. DJL is framework agnostic; it abstracts away commonly used deep-learning functions, using Java Native Access (JNA) on top of existing deep-learning frameworks, currently providing implementations for Apache MXNet and TensorFlow.
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Microsoft Introduces Power Virtual Agents, a No-Code Solution to Building AI Bots
In a recent blog post, Microsoft announced the general availability (GA) of Power Virtual Agents, a service designed to democratize building conversational chatbots using a no-code graphical user interface. The service is part of the Microsoft Power Platform, which includes Power Apps, Power BI and Power Automate and democratizes access to building artificial intelligence-powered bots.