InfoQ Homepage Model Content on InfoQ
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Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how CPUs and GPUs can be utilized.
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How to Operationalize Transformer Models on the Edge
Cassie Breviu discusses different model deployment architectures, how to deploy with edge devices and inference in different programming languages.
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The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
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Unified MLOps: Feature Stores and Model Deployment
Monte Zweben proposes a whole new approach to MLOps that allows to scale models without increasing latency by merging a database, a feature store, and machine learning.
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Iterating on Models on Operating ML
Monte Zweben and Roland Meertens discuss the challenges in building, maintaining, and operating machine learning models.
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Deep Learning at Scale: Distributed Training and Hyperparameter Search for Image Recognition Problems
Michael Shtelma discusses methods and libraries for training models on a dataset that does not fit into memory or maybe even on the disk using multiple GPUs or even nodes.
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From Spark to Elasticsearch and Back - Learning Large-Scale Models for Content Recommendation
Sonya Liberman shares an algorithmic architecture that enables running complex models under difficult scale constraints and shortens the cycle between research and production.
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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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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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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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Petastorm: A Light-Weight Approach to Building ML Pipelines
Yevgeni Litvin describes how Petastorm facilitates tighter integration between Big Data and Deep Learning worlds, simplifies data management and data pipelines, and speeds up model experimentation.
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Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
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