The importance of transparency and user control in machine learning
The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.
Data
Our take on the ideas, information, and tools that make data work.
Data engineers vs. data scientists
The two positions are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity.
What machine learning engineers need to know
The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.
How to train and deploy deep learning at scale
The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.
Sprouted clams and stanky bean: When machine learning makes mistakes
Using silly data sets as examples, Janelle Shane talks about ways that algorithms fail.
Differentiating via data science
Eric Colson explains why companies must now think very differently about the role and placement of data science in organizations.
Inclusivity for the greater good
Ajey Gore explains why GO-JEK is focusing its attention beyond urban Indonesia to help people across the country’s rural areas.
Lessons in Google search data
Seth Stephens-Davidowitz explains how to use Google searches to uncover behaviors or attitudes that may be hidden from traditional surveys.
Merging human and machine learning for everyday solutions
Li Fan shows how Pinterest is using AI to predict what’s in an image, what a user wants, and what they’ll want next.
Privacy in the age of machine learning
Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products.
Crisis Text Line data usage and insights
Nancy Lublin and Bob Filbin explore findings from crisis data.
Defining responsible data practices: A community-driven approach
Natalie Evans Harris discusses the Community Principles on Ethical Data Practices (CPEDP), a code of ethics for data collection, sharing, and utilization.
Data science in the cloud
Alex Smola shares lessons learned from AWS SageMaker, an integrated framework for handling all stages of analysis.
Highlights from the Strata Data Conference in San Jose 2018
Watch highlights covering machine learning, business intelligence, data privacy, and more. From the Strata Data Conference in San Jose 2018.
Using machine learning to monitor and optimize chatbots
The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.
Unleashing the potential of reinforcement learning
The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.
Graphs as the front end for machine learning
The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.
Machine learning needs machine teaching
The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.
How machine learning can be used to write more secure computer programs
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
We need to build machine learning tools to augment machine learning engineers
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
Bringing AI into the enterprise
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
8 fintech trends on our radar
AI, blockchain, payment regionalization, and other fintech trends to watch.
What lies ahead for data
How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape the data world.
How machine learning will accelerate data management systems
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.