All Episodes
O'Reilly Radar — 443 episodes · page 1 of 5
Machine Learning for Operational Analytics and Business Intelligence
Machine Learning and Analytics for Time Series Data
Understanding Deep Neural Networks
Becoming a Machine Learning Practitioner
Labeling, Transforming, and Structuring Training Data Sets for Machine Learning
Taming Chaos: Preparing for Your Next Incident
Make Data Science More Useful
Acquiring and Sharing High-Quality Data
Tools for Machine Learning Development
Enabling End-to-End Machine Learning Pipelines in Real-World Applications
How to Get Started With Site Reliability Engineering (SRE)
Bringing Scalable Real-time Analytics to the Enterprise
Applications of Data Science and Machine Learning in Financial Services
Real-Time Entity Resolution Made Accessible
Why Companies are in Need of Data Lineage Solutions
What Data Scientists and Data Engineers Can Do with Current Generation Serverless Technologies
It's Time for Data Scientists to Collaborate With Researchers in Other Disciplines
Algorithms Are Shaping Our Lives—Here's How We Wrest Back Control
Why Your Attention is Like a Piece of Contested Territory
The Technical, Societal, and Cultural Challenges That Come With the Rise of Fake Media
Using Machine Learning and Analytics to Attract and Retain Employees
How Machine Learning Impacts Information Security
In the Age of AI, Fundamental Value Resides in Data
Trends in Data, Machine Learning, and AI
Tools for Generating Deep Neural Networks With Efficient Network Architectures
Building Tools for Enterprise Data Science
Lessons Learned While Helping Enterprises Adopt Machine Learning
Machine Learning on Encrypted Data
How Social Science Research Can Inform the Design of AI Systems
Why It's Hard to Design Fair Machine Learning Models
Kelsey Hightower and Chris Gaun on Serverless and Kubernetes
Using Machine Learning to Improve Dialog Flow in Conversational Applications
Building Accessible Tools for Large-Scale Computation and Machine Learning
Developers Worldwide Will Collaborate on Solutions to Save Lives in Call For Code
Simplifying Machine Learning Lifecycle Management
How Privacy-Preserving Techniques Can Lead to More Robust Machine Learning Models
Specialized Hardware for Deep Learning Will Unleash Innovation
How to Choose and Deploy a Service Mesh
Data Regulations and Privacy Discussions are Still in the Early Stages
Managing Risk in Machine Learning Models
The Real Value of Data Requires a Holistic View of the End- to-End Data Pipeline
From Chaos Engineering to Bug Bounties—Why Companies Need to Experiment with New Technology
The Evolution of Data Science, Data Engineering, and AI
Cory Doctorow on the Fight for a Configurable and Free Internet
Brian Holt on Learning Javascript and Understanding How to Scale a Website
Kubernetes and Containers for Enterprise Developers
The Web Is Broken; Lets Fix It
Companies in China Are Moving Quickly to Embrace AI Technologies
Teaching and Implementing Data Science and AI in the Enterprise
Stephen Gates on the Growing Risks Posed by Malicious Bots
Simon Moss on Using Artificial Intelligence to Fight Financial Crimes
The Importance of Transparency and User Control in Machine Learning
What Machine Learning Engineers Need to Know
Rebecca Parsons on Evolutionary Architecture
How to Train and Deploy Deep Learning at Scale
Tobi Knaup and Gou Rao on Stateful Containers
Using Machine Learning to Monitor and Optimize Chatbots
Unleashing the Potential of Reinforcement Learning
Gary Sloper on the Evolving Role of DNS in Building Resilient Infrastructure
Graphs as the Front End for Machine Learning
Machine Learning Needs Machine Teaching
Atif Kureishy on How Enterprises are Investing in AI
How Machine Learning Can Be Used to Write More Secure Computer Programs
Bringing AI Into the Enterprise
How Machine Learning Will Accelerate Data Management Systems
Machine Learning at Spotify: You Are What You Stream
Rich Smith on Redefining Success for Security Teams and Managing Security Culture
Taking Banking Into the Future With Lean Analytics and AI
Associative Memory AI: Enabling Machines to Fight Financial Crime With Human-Like Reasoning
What It Means to Be Data-Driven in the Media Industry
The Current State of Apache Kafka
Christie Terrill on Building a High-Caliber Security Program in 90 Days
Building a Natural Language Processing Library for Apache Spark
Susan Sons on Building Security from First Principles
Amit Vij on GPU-Accelerated Analytics Databases
Machine Intelligence for Content Distribution, Logistics, Smarter Cities, and More
Charles Givre on the Impetus for Training All Security Teams in Basic Data Science
Building Data Lifecycles for IoT Projects With Edge Analytics
Andrea Limbago on the Effects of Security's Branding Problem
Vehicle-to-Vehicle Communication Networks Can Help Fuel Smart Cities
Artificial Intelligence and Advances in Data Warehousing
Damon Feldman on Combining a Data Hub and Data Lake
Enterprises are the Natural Environment for AI Deployments
Window Snyder on the Indispensable Human Element in Securing Your Environment
Transforming Organizations Through Analytics Centers of Excellence
Load, Search, and Secure Data in Multiple Formats
The State Of Machine Learning In Apache Spark
Chris Wysopal on a Shared Responsibility Model for Developers and Defenders
Effective Mechanisms for Searching the Space of Machine Learning Algorithms
Scott Roberts on Intelligence-Driven Incident Response
How Ray Makes Continuous Learning Accessible and Easy to Scale
Julie Stanford on Vetting Designs Through Rapid Experimentation
Jack Daniel on Building Community and Historical Context in InfoSec
Integrating Data with AI
Why AI and Machine Learning Researchers are Beginning to Embrace PyTorch
Jay Jacobs on Data Analytics and Security
John Whalen on Using Brain Science in Design
Chris Stetson on System Migrations and Defining a Microservices Reference Architecture
How Big Data and AI Will Reshape the Automotive Industry
Edward Callahan on Reactive Microservice Deployments