Ben Lorica is the former Chief Data Scientist at O’Reilly Media, and the former Program Chair of: the Strata Data Conference, the O’Reilly Artificial Intelligence Conference, and TensorFlow World. Ben is also an advisor to a few exciting startups and organizations: Databricks, Alluxio, Matroid, Anodot, Determined AI, Anyscale.io, Faculty.ai , Graphistry, Yakit, and The Center for Data Intensive Science + Open Commons Consortium (University of Chicago). He is the host and organizer of thedataexchange.media podcast.
Ben Lorica
Radar podcast
Generative AI in the Real World: The LLMOps Shift with Abi Aryan
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Generative AI in the Real World: Laurence Moroney on AI at the Edge
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Radar article
Think Smaller: The Counterintuitive Path to AI Adoption
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Generative AI in the Real World: Chris Butler on GenAI in Product Management
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Generative AI in the Real World: Context Engineering with Drew Breunig
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Generative AI in the Real World: Emmanuel Ameisen on LLM Interpretability
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Generative AI in the Real World: Faye Zhang on Using AI to Improve Discovery
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Generative AI in the Real World: Luke Wroblewski on When Databases Talk Agent-Speak
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Generative AI in the Real World: Understanding A2A with Heiko Hotz and Sokratis Kartakis
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Generative AI in the Real World: Jay Alammar on Building AI for the Enterprise
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Generative AI in the Real World: Phillip Carter on Where Generative AI Meets Observability
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Generative AI in the Real World: Raiza Martin on Building AI Applications for Audio
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Generative AI in the Real World: Stefania Druga on Designing for the Next Generation
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Generative AI in the Real World: Douwe Kiela on Why RAG Isn’t Dead
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Generative AI in the Real World: Danielle Belgrave on Generative AI in Pharma and Medicine
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Generative AI in the Real World: The Startup Opportunity with Gabriela de Queiroz
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Generative AI in the Real World: Securing AI with Steve Wilson
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Generative AI in the Real World: Shreya Shankar on AI for Corporate Data Processing
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Generative AI in the Real World: Vibe Coding with Steve Yegge
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Generative AI in the Real World: Interactions Between Humans and AI with Rajeshwari Ganesan
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Generative AI in the Real World: Getting Beyond the Demo with Hamel Husain
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Generative AI in the Real World: Agents—The Next Step in AI with Shelby Heinecke
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Generative AI in the Real World: Measuring Skills with Kian Katanforoosh
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Generative AI in the Real World: Chloé Messdaghi on AI Security, Policy, and Regulation
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Generative AI in the Real World: Tom Smoker on Getting Started with GraphRAG
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Generative AI in the Real World: Robert Nishihara on AI and the Future of Data
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Generative AI in the Real World: Getting Ahead of the Curve with Claire Vo
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Generative AI in the Real World: The Future of Programming with Matt Welsh
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Generative AI in the Real World: Kingsley Ndoh on Improving Cancer Care with AI
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Generative AI in the Real World: Putting AI in the Hands of Farmers with Rikin Gandhi
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Generative AI in the Real World: Adopting AI in the Enterprise with Timothy Persons
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Generative AI in the Real World: Learning How to Do AI Effectively with Alfred Spector
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Generative AI in the Real World: Andrew Ng on where AI is headed. It’s about agents.
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Generative AI in the Real World: Democratizing AI with Gwendolyn Stripling
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Generative AI in the Real World: Competing in a Generative World with Justin Norman
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Generative AI in the Real World: Pete Warden on Running AI on Small Systems
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Generative AI in the Real World: Chip Huyen on Finding Business Use Cases for Generative AI
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Radar article
The road to Software 2.0
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A world of deepfakes
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Building and deploying AI applications and systems at scale
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Machine learning for operational analytics and business intelligence
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Machine learning and analytics for time series data
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Recent trends in data and machine learning technologies
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Understanding deep neural networks
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How new tools in data and AI are being used in health care and medicine
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Becoming a machine learning practitioner
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One simple chart: Who is interested in Apache Pulsar?
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How organizations are sharpening their skills to better understand and use AI
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Labeling, transforming, and structuring training data sets for machine learning
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Radar article
Got speech? These guidelines will help you get started building voice applications
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Make data science more useful
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One simple graphic: Researchers love PyTorch and TensorFlow
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Acquiring and sharing high-quality data
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Managing machine learning in the enterprise: Lessons from banking and health care
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Tools for machine learning development
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RISELab’s AutoPandas hints at automation tech that will change the nature of software development
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One simple chart: Who is interested in Spark NLP?
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AI and machine learning will require retraining your entire organization
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Enabling end-to-end machine learning pipelines in real-world applications
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What are model governance and model operations?
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The quest for high-quality data
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AI adoption is being fueled by an improved tool ecosystem
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Bringing scalable real-time analytics to the enterprise
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Applications of data science and machine learning in financial services
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Becoming a machine learning company means investing in foundational technologies
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How AI and machine learning are improving customer experience
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Real-time entity resolution made accessible
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Sustaining machine learning in the enterprise
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Why companies are in need of data lineage solutions
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Checking in on AI tools
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What data scientists and data engineers can do with current generation serverless technologies
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Content article
Specialized tools for machine learning development and model governance are becoming essential
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It’s time for data scientists to collaborate with researchers in other disciplines
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Sustaining machine learning in the enterprise
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Algorithms are shaping our lives—here’s how we wrest back control
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Why your attention is like a piece of contested territory
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Content article
You created a machine learning application. Now make sure it’s secure.
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The evolution and expanding utility of Ray
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Three surveys of AI adoption reveal key advice from more mature practices
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The technical, societal, and cultural challenges that come with the rise of fake media
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Core technologies and tools for AI, big data, and cloud computing
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Artificial intelligence and machine learning adoption in European enterprise
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Using machine learning and analytics to attract and retain employees
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How companies are building sustainable AI and ML initiatives
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How machine learning impacts information security
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Overcoming barriers to AI adoption
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9 AI trends on our radar
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7 data trends on our radar
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In the age of AI, fundamental value resides in data
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Content article
Deep automation in machine learning
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Tools for generating deep neural networks with efficient network architectures
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Assessing progress in automation technologies
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Building tools for enterprise data science
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Managing risk in machine learning
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Lessons learned while helping enterprises adopt machine learning
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Machine learning on encrypted data
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How social science research can inform the design of AI systems
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The state of automation technologies
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Why it’s hard to design fair machine learning models
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Why it’s hard to design fair machine learning models
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Using machine learning to improve dialog flow in conversational applications
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Preserving privacy and security in machine learning
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Unlocking innovation in AI
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Building accessible tools for large-scale computation and machine learning
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Simplifying machine learning lifecycle management
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Content article
Notes from the first Ray meetup
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3 promising areas for AI skills development
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5 findings from O’Reilly’s machine learning adoption survey companies should know
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How privacy-preserving techniques can lead to more robust machine learning models
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How companies can get started with AI
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How to take machine learning from exploration to implementation
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Specialized hardware for deep learning will unleash innovation
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Data collection and data markets in the age of privacy and machine learning
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What machine learning means for software development
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Data regulations and privacy discussions are still in the early stages
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Managing risk in machine learning models
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How to think about AI and machine learning technologies, and their roles in automation
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The real value of data requires a holistic view of the end-to-end data pipeline
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The evolution of data science, data engineering, and AI
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Building a stronger data ecosystem
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A new benchmark suite for machine learning
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Companies in China are moving quickly to embrace AI technologies
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How to build analytic products in an age when data privacy has become critical
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Understanding automation
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Teaching and implementing data science and AI in the enterprise
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Building tools for the AI applications of tomorrow
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The importance of transparency and user control in machine learning
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4 things business leaders should know as they explore AI and deep learning
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How companies around the world apply machine learning
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What machine learning engineers need to know
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How to train and deploy deep learning at scale
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Privacy in the age of machine learning
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What happens when AI experts from Silicon Valley and China meet
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Using machine learning to monitor and optimize chatbots
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Unleashing the potential of reinforcement learning
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Graphs as the front end for machine learning
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Machine learning needs machine teaching
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Content article
Introducing RLlib: A composable and scalable reinforcement learning library
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How machine learning can be used to write more secure computer programs
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Put machine learning to work in the real world
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We need to build machine learning tools to augment machine learning engineers
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5 AI trends to watch in 2018
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Bringing AI into the enterprise
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8 fintech trends on our radar
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What lies ahead for data
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How machine learning will accelerate data management systems
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The state of AI adoption
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Practical applications of reinforcement learning in industry
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Machine learning at Spotify: You are what you stream
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Responsible deployment of machine learning
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The current state of Apache Kafka
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Building a natural language processing library for Apache Spark
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Machine intelligence for content distribution, logistics, smarter cities, and more
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How companies can navigate the age of machine learning
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Vehicle-to-vehicle communication networks can help fuel smart cities
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Transforming organizations through analytics centers of excellence
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The age of machine learning
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The state of AI adoption
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The state of machine learning in Apache Spark
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Effective mechanisms for searching the space of machine learning algorithms
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The current state of applied data science
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How Ray makes continuous learning accessible and easy to scale
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Why continuous learning is key to AI
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Why AI and machine learning researchers are beginning to embrace PyTorch
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How big data and AI will reshape the automotive industry
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A framework for building and evaluating data products
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Building a next-generation platform for deep learning
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A scalable time-series database that supports SQL
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Programming collective intelligence for financial trading
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Creating large training data sets quickly
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What are machine learning engineers?
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Data science and deep learning in retail
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Language understanding remains one of AI’s grand challenges
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Data preparation in the age of deep learning
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Scaling machine learning
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Architecting and building end-to-end streaming applications
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Becoming a machine learning engineer
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Natural language analysis using Hierarchical Temporal Memory
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Saving the world—or at least the world’s scientific and government data
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Deep learning that’s easy to implement and easy to scale
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Building machine learning solutions that can withstand adversarial attacks
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Deep learning for Apache Spark
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The key to building deep learning solutions for large enterprises
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Content article
Use deep learning on data you already have
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How big compute is powering the deep learning rocket ship
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7 AI trends to watch in 2017
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8 data trends on our radar for 2017
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2017 will be the year the data science and big data community engage with AI technologies
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Data is only as valuable as the decisions it enables
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Introducing model-based thinking into AI systems
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Building the next-generation big data analytics stack
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Visual tools for overcoming information overload
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Why businesses should pay attention to deep learning
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Understanding predictive analytics
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The technology behind self-driving vehicles
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Data architectures for streaming applications
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Data science for humans and data science for machines
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The importance of emotion in AI systems
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Building human-assisted AI applications
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Enabling enterprise adoption of AI technologies
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Using Agile development techniques for data science projects
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Content article
3 ideas to add to your data science toolkit
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The next 10 years of Apache Hadoop
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Commercial speech recognition systems in the age of big data and deep learning
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Building intelligent applications with deep learning and TensorFlow
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Radar article
What is Artificial Intelligence?
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Hybrid transactional/analytic systems and the quest for database nirvana
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Using AI to build a comprehensive database of knowledge
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Structured streaming comes to Apache Spark 2.0
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Building and deploying large-scale machine learning applications
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Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
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Practical machine learning techniques for building intelligent applications
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Content article
The next 10 years of Apache Hadoop
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Democratizing business analytics
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Stream processing and messaging systems for the IoT age
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Using Apache Spark to predict attack vectors among billions of users and trillions of events
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Metadata services can lead to performance and organizational improvements
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Building a business that combines human experts and data science
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Content article
Compressed representations in the age of big data
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Is 2016 the year you let robots manage your money?
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10 data trends on our radar for 2016
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Investing in big data technologies
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Building a scalable platform for streaming updates and analytics
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Graph databases are powering mission-critical applications
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Jai Ranganathan on architecting big data applications in the cloud
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Building systems for massive scale data applications
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Content article
Ask your data new questions
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Turning big data into actionable insights
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How intelligent data platforms are powering smart cities
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We need open and vendor-neutral metadata services
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Resolving transactional access and analytic performance trade-offs
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Specialized and hybrid data management and processing engines
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Building enterprise data applications with open source components
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From search to distributed computing to large-scale information extraction
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Content article
Showcasing the real-time processing revival
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Learning Paths: a new way to build data skills
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6 reasons why I like KeystoneML
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Content article
Why data preparation frameworks rely on human-in-the-loop systems
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Building self-service tools to monitor high-volume time-series data
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Apache Spark: Powering applications on-premise and in the cloud
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Announcing Cassandra certification
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Data science makes an impact on Wall Street
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The tensor renaissance in data science
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More tools for managing and reproducing complex data projects
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Building big data systems in academia and industry
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A real-time processing revival
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Let’s build open source tensor libraries for data science
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Turning Ph.D.s into industrial data scientists
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Topic models: Past, present, and future
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Content article
Network structure and dynamics in online social systems
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The evolution of GraphLab
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Building and deploying large-scale machine learning pipelines
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A brief look at data science’s past and future
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Content article
Lessons from next-generation data wrangling tools
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Apache Spark’s journey from academia to industry
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Content article
Building Apache Kafka from scratch
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The science of moving dots: The O’Reilly Data Show Podcast
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Big data’s big ideas
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Active learning: Best practices for creating labeled data sets
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Scaling up data frames
Content article
There are many use cases for graph databases and analytics
Content article
Streamlining feature engineering
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A growing number of applications are being built with Spark
Content article
Welcome to Intelligence Matters
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What I use for data visualization
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IPython: A unified environment for interactive data analysis
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Six reasons why I recommend scikit-learn
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Data scientists and data engineers like Python and Scala
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Data wrangling gets a fresh look
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How companies are using Spark
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Stream processing and mining just got more interesting
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How Twitter monitors millions of time series
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Data analysis: Just one component of the data science workflow
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Data analysis tools target non-experts
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Interactive big data analysis using approximate answers
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Surfacing anomalies and patterns in machine data
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Data scientists tackle the analytic lifecycle
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Improving options for unlocking your graph data
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11 essential features that visual analysis tools should have
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Tachyon: An open source, distributed, fault-tolerant, in-memory file system
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The re-emergence of time-series
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Python data tools just keep getting better
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Shark: Real-time queries and analytics for big data
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