InfoQ Homepage Machine Learning Content on InfoQ
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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.
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Google Cloud Team Releases AutoML Natural Language
The Google Cloud team recently announced the generally available (GA) release of AutoML Natural Language framework. AutoML Natural Language supports features for data processing and common machine learning tasks like classification, sentiment analysis, and entity extraction.
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Amazon Releases SageMaker Studio IDE for Machine Learning
At the recent re:Invent conference, Amazon Web Services (AWS) announced Amazon SageMaker Studio, an integrated development enviornment (IDE) for machine learning (ML) that brings code editing, training job tracking and tuning, and debugging all into a single web-based interface.
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Recap of AWS re:Invent 2019
Last week in Las Vegas, AWS held their annual re:Invent conference and unveiled a slew of new products, while updating many existing ones. Here's a review of announcements impacting compute, data and storage, app integration, networking, machine learning, identity management, enterprise services, and development.
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LG Releases New Hyperparameter Optimization Framework Called Auptimizer
Scientists at LG’s Advanced AI division released Auptimizer, an open-source framework for hyperparameter optimization of machine learning models. The software focuses on job distribution, scheduling and bookkeeping associated with performing hyperparameter optimization at scale, relying on existing packages for optimization algorithms.
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Michael Berthold on End-to-End Data Science Using KNIME Software
Open source data analytics platform KNIME CEO and co-founder Michael Berthold gave the keynote presentation at this year's KNIME Fall Summit 2019 Conference. He spoke about the end-to-end data science cycle. The data science process lifecycle mainly involves create and productionize categories.
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Data Science at the Intersection of Emerging Technologies
Kirk Borne, principal data scientist at Booz Allen Hamilton, gave a keynote presentation at this year’s Oracle Code One Conference on how the connection between emerging technologies, data, and machine learning are transforming data into value. Emerging technological innovations like AI, robotics, computer vision and more, are enabled by data and create value from data.
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DataOps and Operations-Centric Data Architecture
Eric Estabrooks from DataKitchen spoke at this year's Data Architecture Summit 2019 Conference about how DevOps tasks should be managed for data architecture. DataOps is a collaborative data management practice and is emerging as an area of interest in the industry.
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Machine Learning on Mobile and Edge Devices with TensorFlow Lite: Daniel Situnayake at QCon SF
At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on mobile devices and embedded systems, and was the main topic of the presentation.
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Microsoft Announces Azure Synapse for Data Warehousing and Analytics
During Microsoft's annual Ignite conference the company announced a new analytics service called Azure Synapse. The service, which is a continuation of Azure SQL Data Warehouse, focuses on bringing enterprise data warehousing and big data analytics into a single service.