InfoQ Homepage Machine Learning Content on InfoQ
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ML Panel: "ML in Production - What's Next?"
The panelists discuss lessons learned with putting ML systems into production, what is working and what is not working, building ML teams, dealing with large datasets, governance and ethics/privacy.
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Machine Learning at the Edge
Katharine Jarmul discusses utilizing new distributed data science and machine learning models, such as federated learning, to learn from data at the edge.
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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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MLOps: the Most Important Piece in the Enterprise AI Puzzle
Francesca Lazzeri overviews the latest MLOps technologies and principles that data scientists and ML engineers can apply to their machine learning processes.
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Developing and Deploying ML across Teams with MLOps Automation Tool
Fabio Grätz and Thomas Wollmann discuss the MLOps Automation tool, and how it can be used to perform DevOps tasks on ML across teams.
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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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Panel: Future of Language Support for ML
Jendrik Jördening, Irene Dea, Alanna Tempest take a look at the state of the art of ML/AI development and how advances in language technology (specifically differentiable programming langs) can help.
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Designing Better ML Systems: Learnings from Netflix
Savin Goyal shares lessons learned by Netflix building their ML infrastructure, and some of the tradeoffs to consider when designing or buying a machine learning system.
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BERT for Sentiment Analysis on Sustainability Reporting
Susanne Groothuis discusses how KPMG created a custom sentiment analysis model capable of detecting subtleties, and provides them with a metric indicating the balance of a report.
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Applying Machine Learning to Financial Payments
Tamsin Crossland discusses how ML can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
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The Fast Track to AI with JavaScript and Serverless
Peter Elger explores how to get started building AI enabled platforms and services using full stack JavaScript and Serverless technologies.
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Is Machine Learning the Right Tool?
Brian Korzynski discusses when and where using machine learning will fit within projects.
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