The road to Software 2.0
It’s clear that AI can and will have a big influence on how we develop software.
Ben Lorica
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.
Radar
A world of deepfakes
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Building and deploying AI applications and systems at scale
Ben Lorica and Roger Chen review how companies are building AI applications today.
Recent trends in data and machine learning technologies
Ben Lorica dives into emerging technologies for building data infrastructures and machine learning platforms.
How new tools in data and AI are being used in health care and medicine
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
How organizations are sharpening their skills to better understand and use AI
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Got speech? These guidelines will help you get started building voice applications
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
Managing machine learning in the enterprise: Lessons from banking and health care
A look at how guidelines from regulated industries can help shape your ML strategy.
RISELab’s AutoPandas hints at automation tech that will change the nature of software development
Neural-backed generators are a promising step toward practical program synthesis.
One simple chart: Who is interested in Spark NLP?
As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.
AI and machine learning will require retraining your entire organization
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
What are model governance and model operations?
A look at the landscape of tools for building and deploying robust, production-ready machine learning models.
The quest for high-quality data
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.
AI adoption is being fueled by an improved tool ecosystem
We now are in the implementation phase for AI technologies.
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.
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.
Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Checking in on AI tools
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
The evolution and expanding utility of Ray
There are growing numbers of users and contributors to the framework, as well as libraries for reinforcement learning, AutoML, and data science.
Three surveys of AI adoption reveal key advice from more mature practices
An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
Artificial intelligence and machine learning adoption in European enterprise
How companies in Europe are preparing for and adopting AI and ML technologies.
How companies are building sustainable AI and ML initiatives
A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.
Overcoming barriers to AI adoption
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.
9 AI trends on our radar
How new developments in automation, machine deception, hardware, and more will shape AI.
7 data trends on our radar
From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.
Managing risk in machine learning
Considerations for a world where ML models are becoming mission critical.
The state of automation technologies
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Why it’s hard to design fair machine learning models
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Preserving privacy and security in machine learning
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
Unlocking innovation in AI
Ben Lorica and Roger Chen provide a glimpse into tools and trends poised to accelerate AI innovation.
3 promising areas for AI skills development
O'Reilly survey results and usage data reveal growing trends and topics in artificial intelligence.
5 findings from O’Reilly’s machine learning adoption survey companies should know
New survey results highlight the ways organizations are handling machine learning's move to the mainstream.
How companies can get started with AI
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.
How to take machine learning from exploration to implementation
Recognizing the interest in ML, the Strata Data Conference program is designed to help companies adopt ML across large sections of their existing operations.
What machine learning means for software development
“Human in the loop” software development will be a big part of the future.
How to think about AI and machine learning technologies, and their roles in automation
An overview and framework, including tools that can be used to enable automation.
Building a stronger data ecosystem
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.
Understanding automation
Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.
4 things business leaders should know as they explore AI and deep learning
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
Privacy in the age of machine learning
Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products.
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.
5 AI trends to watch in 2018
From methods to tools to ethics, Ben Lorica looks at what's in store for artificial intelligence.
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.
The state of AI adoption
An overview of adoption, and suggestions to companies interested in AI technologies.
Practical applications of reinforcement learning in industry
An overview of commercial and industrial applications of reinforcement learning.
Responsible deployment of machine learning
Ben Lorica explains how to guard against flaws and failures in your machine learning deployments.
How companies can navigate the age of machine learning
To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models.
The age of machine learning
Ben Lorica discusses the state of machine learning.
The state of AI adoption
AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they've observed in industry.
The current state of applied data science
Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.
Why continuous learning is key to AI
A look ahead at the tools and methods for learning from sparse feedback.
What are machine learning engineers?
A new role focused on creating data products and making data science work in production.
7 AI trends to watch in 2017
From tools, to research, to ethics, Ben Lorica looks at what’s in store for artificial intelligence in 2017.
8 data trends on our radar for 2017
From deep learning to decoupling, here are the data trends to watch in the year ahead.
What is Artificial Intelligence?
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.
How intelligent data platforms are powering smart cities
Smart cities and smart nations run on data.
Big data’s big ideas
From cognitive augmentation to artificial intelligence, here's a look at the major forces shaping the data world.
Content
One simple chart: Who is interested in Apache Pulsar?
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
One simple graphic: Researchers love PyTorch and TensorFlow
Interest in PyTorch among researchers is growing rapidly.
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
You created a machine learning application. Now make sure it’s secure.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
Core technologies and tools for AI, big data, and cloud computing
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
Deep automation in 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.
Assessing progress in automation technologies
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
Notes from the first Ray meetup
Ray is beginning to be used to power large-scale, real-time AI applications.
Data collection and data markets in the age of privacy and machine learning
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.
A new benchmark suite for machine learning
MLPerf is a new set of benchmarks compiled by a growing list of industry and academic contributors.
How to build analytic products in an age when data privacy has become critical
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.
Building tools for the AI applications of tomorrow
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
How companies around the world apply machine learning
Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security.
What happens when AI experts from Silicon Valley and China meet
Why we're taking the AI Conference to Beijing.
Introducing RLlib: A composable and scalable reinforcement learning library
RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning.
Put machine learning to work in the real world
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.
Use deep learning on data you already have
Putting deep learning into practice with new tools, frameworks, and future developments.
3 ideas to add to your data science toolkit
Techniques to address overfitting, hyperparameter tuning, and model interpretability.
The next 10 years of Apache Hadoop
Doug Cutting, Tom White, and Ben Lorica explore Hadoop's role over the coming decade.
The next 10 years of Apache Hadoop
Apache Hadoop co-founders Doug Cutting and Mike Cafarella explore the future of Hadoop.
Compressed representations in the age of big data
Emerging trends in intelligent mobile applications and distributed computing.
10 data trends on our radar for 2016
Promising topics in data that we'll be watching closely in the year ahead.
Ask your data new questions
Consolidating data across silos improves business insight.
We need open and vendor-neutral metadata services
Comprehensive metadata collection and analysis can pave the way for many interesting applications.
Specialized and hybrid data management and processing engines
A new crop of interesting solutions for the complexity of operating multiple systems in a distributed computing setting.
Showcasing the real-time processing revival
Tools and learning resources for building intelligent, real-time products.
Learning Paths: a new way to build data skills
Logical and well-crafted collections of data video courses get you where you need to go.
Why data preparation frameworks rely on human-in-the-loop systems
The O'Reilly Data Show Podcast: Ihab Ilyas on building data wrangling and data enrichment tools in academia and industry.
Apache Spark: Powering applications on-premise and in the cloud
The O'Reilly Data Show Podcast: Patrick Wendell on the state of the Spark ecosystem.
Announcing Cassandra certification
A new partnership between O’Reilly and DataStax offers certification and training in Cassandra.
More tools for managing and reproducing complex data projects
A survey of the landscape shows the types of tools remain the same, but interfaces continue to improve.
A real-time processing revival
Things are moving fast in the stream processing world.
Let’s build open source tensor libraries for data science
Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries.
Network structure and dynamics in online social systems
Understanding information cascades, viral content, and significant relationships.
The evolution of GraphLab
The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.
Building and deploying large-scale machine learning pipelines
We need primitives, pipeline synthesis tools, and most importantly, error analysis and verification.
Lessons from next-generation data wrangling tools
Drawing inspiration from recent advances in data preparation.
Building Apache Kafka from scratch
In this episode of the O'Reilly Data Show Podcast, Jay Kreps talks about data integration, event data, and the Internet of Things.
The science of moving dots: The O’Reilly Data Show Podcast
Rajiv Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.
Active learning: Best practices for creating labeled data sets
Learn simple ways to improve data models by cleaning up and tweaking the distribution of training data.
Scaling up data frames
New frameworks for interactive business analysis and advanced analytics fuel the rise in tabular data objects.
There are many use cases for graph databases and analytics
Business users are becoming more comfortable with graph analytics.
Streamlining feature engineering
Researchers and startups are building tools that enable feature discovery.
A growing number of applications are being built with Spark
Many more companies want to highlight how they're using Apache Spark in production.
Welcome to Intelligence Matters
Casting a critical eye on the exciting developments in the world of AI.
What I use for data visualization
An array of tools for tackling data visualizations.
IPython: A unified environment for interactive data analysis
It has roots in academic scientific computing, but has features that appeal to many data scientists.
Six reasons why I recommend scikit-learn
It's an extensive, well-documented, and accessible, curated library of machine-learning models
Data scientists and data engineers like Python and Scala
Python and Scala are popular among members of several well-attended SF Bay Area Meetups
Data wrangling gets a fresh look
We are in the early days of productivity technology in data science
How companies are using Spark
The inaugural Spark Summit will feature a wide variety of real-world applications
Stream processing and mining just got more interesting
A general purpose stream processing framework from the team behind Kafka and new techniques for computing approximate quantiles.
How Twitter monitors millions of time series
A distributed, near real-time system simplifies the collection, storage, and mining of massive amounts of event data
Data analysis: Just one component of the data science workflow
Specialized tools run the risk of being replaced by others that have more coverage.
Data analysis tools target non-experts
Tools simplify the application of advanced analytics and the interpretation of results
Interactive big data analysis using approximate answers
As data sizes continue to grow, interactive query systems may start adopting the sampling approach central to BlinkDB.
Surfacing anomalies and patterns in machine data
Compelling large-scale data platforms originate from the world of IT Operations
Data scientists tackle the analytic lifecycle
A new crop of data science tools for deploying, monitoring, and maintaining models
Improving options for unlocking your graph data
Graph data is an area that has attracted many enthusiastic entrepreneurs and developers
11 essential features that visual analysis tools should have
Visual analysis tools are adding advanced analytics for big data
Tachyon: An open source, distributed, fault-tolerant, in-memory file system
Tachyon enables data sharing across frameworks and performs operations at memory speed
The re-emergence of time-series
Researchers begin to scale up pattern recognition, machine-learning, and data management tools.
Python data tools just keep getting better
A variety of tools are making data science tasks easy to do in Python
Shark: Real-time queries and analytics for big data
Shark is 100X faster than Hive for SQL, and 100X faster than Hadoop for machine-learning
Seven reasons why I like Spark
Spark is becoming a key part of a big data toolkit.