InfoQ Homepage Machine Learning Content on InfoQ
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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.
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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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Computer Mathematics, AI and Functional Programming
Moa Johansson discusses the history of computer mathematics and how it connects to the development of early functional programming languages like Standard ML.
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Evoking Magic Realism with Augmented Reality Technology
Diana Hu explores how building a real world system is more a software engineering art, requiring making choices among a set of tradeoffs.
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MLflow: An Open Platform to Simplify the Machine Learning Lifecycle
Corey Zumar offers an overview of MLflow – a new open source platform to simplify the machine learning lifecycle from Databricks.
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