Applications of data science and machine learning in financial services
The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Becoming an ML company means investing in foundational tech
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
AI, ML, and a better customer experience
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Real-time entity resolution made accessible
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
Rise of the (advertising) machines
Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications.
Privacy, identity, and autonomy in the age of big data and AI
Sandra Wachter argues that a right to reasonable inferences could protect against new forms of discrimination.
Sustaining ML in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Making the future
James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.
Highlights from the Strata Data Conference in London 2019
Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more.
Looking Back on the O’Reilly Artificial Intelligence Conference
More than anything else, O'Reilly's AI Conference was about making the leap to AI 2.0.
Why companies are in need of data lineage solutions
The O’Reilly Data Show Podcast: Neelesh Salian on data lineage, data governance, and evolving data platforms.
Automating ethics
Machines will need to make ethical decisions, and we will be responsible for those decisions.
Simple, scalable, and sustainable: A methodical approach to AI adoption
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
Software 2.0 and Snorkel
Christopher Ré discusses Snorkel, a system for fast training data creation.
Computational propaganda
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Artificial intelligence: The “refinery” for data
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
Decoding the human genome with deep learning
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
Applied machine learning at Facebook
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
Making real-world distributed deep learning easy with Nauta
Carlos Humberto Morales offers an overview of Nauta, an open source multiuser platform that lets data scientists run complex deep learning models on shared hardware.
Automation of AI: Accelerating the AI revolution
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks.
Data fueling AI of the future
Thomas Henson considers how AI will shape the experiences of future generations.
Toward ethical AI: Inclusivity as a messy, difficult, but promising answer
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
Automated ML: A journey from CRISPR.ML to Azure ML
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale
Gadi Singer discusses the major questions organizations confront as they integrate deep learning.