InfoQ Homepage Machine Learning Content on InfoQ
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ML/AI Panel
The panelists discuss what makes ML different from other types of applications and why it requires special tooling.
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Swift for Tensorflow
Paige Bailey demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.
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Breakthroughs and the Future of (Deep) Reinforcement Learning
Andreas Bühlmeier discusses the foundation of Reinforced Learning and demonstrates how it is implemented. Also, he shows how to track and understand a system’s learning progress.
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ML's Hidden Tasks: A Checklist for Developers When Building ML Systems
Jade Abbott discusses the set of unexpected things that go on the "take it to production" checklist in the case of machine learning, and what are the tools that can help.
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CI/CD for Machine Learning
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability.
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Machine Learning 101
Grishma Jena gives an overview of ML and delves deep into the pipeline used - right from fetching the data, the tools and frameworks used to creating models, gaining insights and telling a story.
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ML in the Browser: Interactive Experiences with Tensorflow.js
Victor Dibia provides a friendly introduction to machine learning, covers concrete steps on how front-end developers can create their own ML models and deploy them as part of web applications.
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When Machine Learning Can't Replace the Human
Pamela Gay explores how creative software solutions let scientists explore the solar system.
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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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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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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.
