InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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A Look at the Methods to Detect and Try to Remove Bias in Machine Learning Models
Thierry Silbermann explores some examples where machine learning fails and/or is making a negative impact, looking at some of the tools available today to fix the model.
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Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
Mayank Bansal and Apoorva Jindal present Peloton, a Unified Resource Scheduler for collocating heterogeneous workloads in shared Mesos clusters.
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Advanced Data Visualizations in Jupyter Notebooks
Chakri Cherukuri discusses how to build advanced data visualization applications and interactive plots in Jupyter notebooks, including use cases with time series analysis.
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Datadog: a Real-Time Metrics Database for One Quadrillion Points/Day
Ian Nowland & Joel Barciauskas talk about the challenges Datadog faces, how the architecture has evolved, and what they are looking to in the future as they architect for a quadrillion points per day.
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Scaling DB Access for Billions of Queries Per Day @PayPal
Petrica Voicu and Kenneth Kang talk about Hera (High Efficiency Reliable Access to data stores) – an open-source in Go – and how it helps PayPal to manage database access and deal with issues.
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Announcing Broadway
José Valim discusses how Broadway connects multiple stages and producers, how it leverages GenStage to provide back-pressure, and other features such as batching, rate-limiting, partitioning and more.
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Automating Software Development with Deep Learning
Emil Wallner discusses the state of the art in software development automation, its current weaknesses, and areas that are ready for production.
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CockroachDB: Architecture of a Geo-Distributed SQL Database
Peter Mattis talks about how Cockroach Labs addressed the complexity of distributed databases with CockroachDB and gives a tour of CockroachDB’s internals.
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Metrics-Driven Machine Learning Development at Salesforce Einstein
Eric Wayman discusses how Salesforce tracks data and modeling metrics in the pipeline to identify data and modeling issues and to raise alerts for issues affecting models running in production.
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Seven Steps to Design, Build, and Scale an AI Product
Allie Miller explores the fundamental use cases in AI and how designers and engineers can be at the forefront of prioritizing AI/ML best practices.
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Automating Machine Learning and Deep Learning Workflows
Mourad Mourafiq discusses automating ML workflows with the help of Polyaxon, an open source platform built on Kubernetes, to make machine learning reproducible, scalable, and portable.
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Panel: ML for Developers/SWEs
The panelists cover how they've adopted applied machine learning to software engineering.
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