It’s clear that AI can and will have a big influence on how we develop software.
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Ben Lorica and Roger Chen review how companies are building AI applications today.
Ben Lorica dives into emerging technologies for building data infrastructures and machine learning platforms.
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
A look at how guidelines from regulated industries can help shape your ML strategy.
Neural-backed generators are a promising step toward practical program synthesis.
As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
A look at the landscape of tools for building and deploying robust, production-ready machine learning models.
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.
We now are in the implementation phase for AI technologies.
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
There are growing numbers of users and contributors to the framework, as well as libraries for reinforcement learning, AutoML, and data science.
An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
How companies in Europe are preparing for and adopting AI and ML technologies.
A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.
The program for our Artificial Intelligence Conference in New York City will showcase tools, best practices, and use cases from companies leading the way in AI adoption.
How new developments in automation, machine deception, hardware, and more will shape AI.
From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.
Considerations for a world where ML models are becoming mission critical.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
Ben Lorica and Roger Chen provide a glimpse into tools and trends poised to accelerate AI innovation.
O'Reilly survey results and usage data reveal growing trends and topics in artificial intelligence.
New survey results highlight the ways organizations are handling machine learning's move to the mainstream.
The program for our Artificial Intelligence Conference in London is structured to help companies that are still very much in the early stages of AI adoption.
Recognizing the interest in ML, the Strata Data Conference program is designed to help companies adopt ML across large sections of their existing operations.
“Human in the loop” software development will be a big part of the future.
An overview and framework, including tools that can be used to enable automation.
Ben Lorica looks at the problems we’re facing as we collect and store data, particularly when our machine learning models require huge amounts of labeled data.
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products.
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
From methods to tools to ethics, Ben Lorica looks at what's in store for artificial intelligence.
AI, blockchain, payment regionalization, and other fintech trends to watch.
How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape the data world.
An overview of adoption, and suggestions to companies interested in AI technologies.
An overview of commercial and industrial applications of reinforcement learning.
Ben Lorica explains how to guard against flaws and failures in your machine learning deployments.
To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models.
Ben Lorica discusses the state of machine learning.
AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they've observed in industry.
Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.
A look ahead at the tools and methods for learning from sparse feedback.
A new role focused on creating data products and making data science work in production.
From tools, to research, to ethics, Ben Lorica looks at what’s in store for artificial intelligence in 2017.
From deep learning to decoupling, here are the data trends to watch in the year ahead.
Mike Loukides and Ben Lorica examine factors that have made AI a hot topic in recent years, today's successful AI systems, and where AI may be headed.
Smart cities and smart nations run on data.
From cognitive augmentation to artificial intelligence, here's a look at the major forces shaping the data world.
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
Interest in PyTorch among researchers is growing rapidly.
Why companies are turning to specialized machine learning tools like MLflow.
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
Ray is beginning to be used to power large-scale, real-time AI applications.
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.
MLPerf is a new set of benchmarks compiled by a growing list of industry and academic contributors.
Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security.
Why we're taking the AI Conference to Beijing.
RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning.
The sessions and training courses at Strata Data San Jose 2018 will focus on practical use cases of machine learning for data scientists, engineers, managers, and executives.
Putting deep learning into practice with new tools, frameworks, and future developments.
Techniques to address overfitting, hyperparameter tuning, and model interpretability.
Doug Cutting, Tom White, and Ben Lorica explore Hadoop's role over the coming decade.
Apache Hadoop co-founders Doug Cutting and Mike Cafarella explore the future of Hadoop.
Emerging trends in intelligent mobile applications and distributed computing.
Promising topics in data that we'll be watching closely in the year ahead.
Consolidating data across silos improves business insight.
Comprehensive metadata collection and analysis can pave the way for many interesting applications.
A new crop of interesting solutions for the complexity of operating multiple systems in a distributed computing setting.
Tools and learning resources for building intelligent, real-time products.
Logical and well-crafted collections of data video courses get you where you need to go.
The O'Reilly Data Show Podcast: Ihab Ilyas on building data wrangling and data enrichment tools in academia and industry.
The O'Reilly Data Show Podcast: Patrick Wendell on the state of the Spark ecosystem.
A new partnership between O’Reilly and DataStax offers certification and training in Cassandra.
A survey of the landscape shows the types of tools remain the same, but interfaces continue to improve.
Things are moving fast in the stream processing world.
Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries.
Understanding information cascades, viral content, and significant relationships.
The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.
We need primitives, pipeline synthesis tools, and most importantly, error analysis and verification.
Drawing inspiration from recent advances in data preparation.
In this episode of the O'Reilly Data Show Podcast, Jay Kreps talks about data integration, event data, and the Internet of Things.
Rajiv Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.
Learn simple ways to improve data models by cleaning up and tweaking the distribution of training data.
New frameworks for interactive business analysis and advanced analytics fuel the rise in tabular data objects.
Business users are becoming more comfortable with graph analytics.
Researchers and startups are building tools that enable feature discovery.
Many more companies want to highlight how they're using Apache Spark in production.
Casting a critical eye on the exciting developments in the world of AI.
An array of tools for tackling data visualizations.
It has roots in academic scientific computing, but has features that appeal to many data scientists.
It's an extensive, well-documented, and accessible, curated library of machine-learning models
Python and Scala are popular among members of several well-attended SF Bay Area Meetups
We are in the early days of productivity technology in data science
The inaugural Spark Summit will feature a wide variety of real-world applications
A general purpose stream processing framework from the team behind Kafka and new techniques for computing approximate quantiles.
A distributed, near real-time system simplifies the collection, storage, and mining of massive amounts of event data
Specialized tools run the risk of being replaced by others that have more coverage.
Tools simplify the application of advanced analytics and the interpretation of results
As data sizes continue to grow, interactive query systems may start adopting the sampling approach central to BlinkDB.
Compelling large-scale data platforms originate from the world of IT Operations
A new crop of data science tools for deploying, monitoring, and maintaining models
Graph data is an area that has attracted many enthusiastic entrepreneurs and developers
Visual analysis tools are adding advanced analytics for big data
Tachyon enables data sharing across frameworks and performs operations at memory speed
Researchers begin to scale up pattern recognition, machine-learning, and data management tools.
A variety of tools are making data science tasks easy to do in Python
Shark is 100X faster than Hive for SQL, and 100X faster than Hadoop for machine-learning
Spark is becoming a key part of a big data toolkit.