InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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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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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From Research to Production with PyTorch
Jeff Smith covers some of the latest features from PyTorch including the TorchScript JIT compiler, distributed data parallel training, TensorBoard integration, new APIs, and more.
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Getting Started in Deep Learning with TensorFlow 2.0
Brad Miro explains what deep learning is, why one may want to use it over traditional ML methods, as well as how to get started building deep learning models using TensorFlow 2.0.
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Hands-on Feature Engineering for Natural Language Processing
Susan Li shares various NLP feature engineering techniques, from Bag-Of-Words to TF-IDF to word embedding that includes feature engineering for both ML models and an emerging deep learning approach.
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EBtree - Design for a Scheduler and Use (Almost) Everywhere
Andjelko Iharos explores the goals, design and the choices behind the implementations of EBtree, and how they produce a very fast and versatile data storage for many of HAProxys advanced features.
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Deep Learning for Recommender Systems
Oliver Gindele discusses how some DL models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.
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