AI adoption is being fueled by an improved tool ecosystem
We now are in the implementation phase for AI technologies.
Ideas
What's on our radar.
How to get started with site reliability engineering (SRE)
Google SRE Stephen Thorne shares best practices for starting an SRE team at your company.
Four short links: 11 June 2019
Premium Firefox, FPGAs for Graph Processing, Decision Framework, and The Online Experience of South Asian Women
Maximizing paper clips
We won’t get the chance to worry about artificial general intelligence if we don’t deal with the problems we have in the present.
Bringing scalable real-time analytics to the enterprise
The O’Reilly Data Show Podcast: Dhruba Borthakur and Shruti Bhat on enabling interactive analytics and data applications against live data.
219+ live online training courses opened for June and July
Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics.
What’s driving open source software in 2019
Cloud native, AI/ML, and data tools and topics are areas of emphasis for the O’Reilly Open Source Software Conference.
Reduce toil through better alerting
How SREs can use a hierarchy for mature alerts.
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.
Becoming a machine learning company means investing in foundational technologies
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
The topics to watch in software architecture
Microservices, serverless, AI, ML, and Kubernetes are among the most notable topics in our analysis of proposals from the O’Reilly Software Architecture Conference.
How AI and machine learning are improving 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.
Making Facebook a scapegoat is a mistake
Breaking up Facebook won't solve the disinformation or privacy problems. It might well make it harder for Facebook to work on those problems.
Toward the next generation of programming tools
Programmers have built great tools for others. It’s time they built some for themselves.
Real-time entity resolution made accessible
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
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The unstoppable rise of white box data
Chris Taggart explains the benefits of “white box data” and outlines the structural shifts that are moving the data world toward this model.
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Rise of the (advertising) machines
Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications.
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Combining creativity and analytics
David Boyle shares lessons on how analysts can harness data and creativity to build partnerships.
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Building data science capacity in your organization
Shingai Manjengwa shares insights from teaching data science to 300,000 online learners.
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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.
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Finding your North Star
Cait O’Riordan discusses the North Star metric the Financial Times uses across the organization to drive subscriber growth.
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Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
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Making data science useful
Cassie Kozyrkov explains how organizations can extract more value from their data.
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The enterprise data cloud
Mick Hollison describes why hybrid and multi-cloud is the future for organizations that want to capitalize on machine learning and AI.
Highlights from the Strata Data Conference in London 2019
Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more.
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Making the future
James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.
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.
How companies adopt and apply cloud native infrastructure
Survey results reveal the path organizations face as they integrate cloud native infrastructure and harness the full power of the cloud.
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.
Stablecoins: Solving the cryptocurrency volatility crisis
Resolving the volatility problem will unlock the groundwork needed for blockchain-based global payment systems.
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Artificial intelligence: The “refinery” for data
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
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Computational propaganda
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
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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.
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Simple, scalable, and sustainable: A methodical approach to AI adoption
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
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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.
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Applied machine learning at Facebook
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
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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.
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Software 2.0 and Snorkel
Christopher Ré discusses Snorkel, a system for fast training data creation.
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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.
Highlights from the O'Reilly Artificial Intelligence Conference in New York 2019
Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.
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How AI adaptive technology can upgrade education
Joleen Liang explains how AI and precise knowledge points can help students learn.
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Checking in on AI tools
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
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AI and the robotics revolution
Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.
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Is AI human-ready?
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
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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.
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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.
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Machine learning for personalization
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
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Data fueling AI of the future
Thomas Henson considers how AI will shape the experiences of future generations.
Strata San Francisco, 2019: Opportunities and Risks
Balancing risk and reward is a necessary tension we'll need to understand as we continue our journey into the age of data.
What data scientists and data engineers can do with current generation serverless technologies
The O’Reilly Data Show Podcast: Avner Braverman on what’s missing from serverless today and what users should expect in the near future.
Why a data scientist is not a data engineer
Or, why science and engineering are still different disciplines.
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
De-biasing language
The toughest bias problems are often the ones you only think you’ve solved.
Highlights from the Strata Data Conference in San Francisco 2019
Watch highlights from expert talks covering AI, machine learning, data analytics, and more.
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Data warehousing is not a use case
Google BigQuery co-creator Jordan Tigani shares his vision for where cloud-scale data analytics is heading.
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Chatting with machines: Strange things 60 billion bot logs say about human nature
Lauren Kunze discusses lessons learned from an analysis of interactions between humans and chatbots.
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The enterprise data cloud
Mike Olson describes the key capabilities an enterprise data cloud system requires, and why hybrid and multi-cloud is the future.
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Forecasting uncertainty at Airbnb
Theresa Johnson outlines the AI powering Airbnb’s metrics forecasting platform.
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Hacking the vote: The neuropolitical universe
Elizabeth Svoboda explains how biosensors and predictive analytics are being applied by political campaigns and what they mean for the future of free and fair elections.
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Winners of the Strata Data Awards 2019
The Strata Data Award is given to the most disruptive startup, the most innovative industry technology, the most impactful data science project, and the most notable open source contribution.
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Likewar: How social media is changing the world and how the world is changing social media.
Peter Singer explores the new rules of power in the age of social media and how we can navigate a world increasingly shaped by "likes" and lies.
It’s time for data scientists to collaborate with researchers in other disciplines
The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.
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Streamlining your data assets: A strategy for the journey to AI
Dinesh Nirmal shares a data asset framework that incorporates current business structures and the elements you need for an AI-fluent data platform.