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Explore real-time streaming featurization through the interoperable Ibis, Substrait, and Apache Arrow software stack.
An overview of cybersecurity threats in the metaverse, from Wi-Fi and privacy vulnerabilities to AI-assisted cyberattacks and virtual policing.
Explains how score-based diffusion models learn data-distribution gradients to synthesize controllable images and support inverse problems and molecular prediction.
Automate machine learning training, evaluation, cloud provisioning, and pull-request reporting by integrating CML with CI/CD workflows.
A practical guide to helping small and medium-sized organizations move machine-learning products from proof of concept to reliable, ethical production.
Explore how to evaluate, filter, harmonize, and debias movement data for reliable location intelligence and high-fidelity analytical use cases.
A practical journey from Responsible AI principles to review questions, governance, fairness, explainability, and privacy tools for healthcare machine learning.
A hands-on guide for leaders to understand AI, work with technical teams, and turn models and data into organizational outcomes.
A roadmap for analysts to transition into data science through self-teaching, formal education, or in-role upskilling.
Explore how electronic health records and natural language processing can turn messy clinical documentation into structured data for surveillance, prediction, and personalized care.
Learn how survival analysis models time-to-event data, handles censoring, and supports prediction and decision-making with Python.
An overview of personalized machine learning, using recommender systems to explain applications in language, vision, and fairness.
Build a reproducible, automated machine-learning workflow with DVC, GitHub, and GitHub Actions, from data and model changes through deployment and scheduled retraining.
An overview of Apache TVM and OctoML for automatically optimizing and packaging machine-learning models across diverse hardware targets.
A strategic framework for scaling machine learning and AI through data understanding, cross-functional teams, production infrastructure, automation, and responsible risk management.
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