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.
Data science
Data science ideas and resources.
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.
Handling real-time data operations in the enterprise
Getting DataOps right is crucial to your late-stage big data projects.
It’s time to establish big data standards
The deployment of big data tools is being held back by the lack of standards in a number of growth areas.
Case studies in data ethics
These studies provide a foundation for discussing ethical issues so we can better integrate data ethics in real life.
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.
Testing machine learning explanation techniques
The importance of testing your tools, using multiple tools, and seeking consistency across various interpretability techniques.
5 key drivers for getting more value from your data
Considerations based on experience with Fortune 500 clients.
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.
How to run a custom version of Spark on hosted Kubernetes
Learn how Spark 2.3.0+ integrates with K8s clusters on Google Cloud and Azure.
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.
It’s time for data ethics conversations at your dinner table
In an era where fake news travels faster than the truth, our communities are at a critical juncture.
Interpreting predictive models with Skater: Unboxing model opacity
A deep dive into model interpretation as a theoretical concept and a high-level overview of Skater.
Data governance and the death of schema on read
Comcast’s system of storing schemas and metadata enables data scientists to find, understand, and join data of interest.
Identifying budding big data talent in your company
How to find promising candidates for upskilling within your organization.
Comparing production-grade NLP libraries: Accuracy, performance, and scalability
A comparison of the accuracy and performance of Spark-NLP vs. spaCy, and some use case recommendations.
Comparing production-grade NLP libraries: Running Spark-NLP and spaCy pipelines
A step-by-step guide to building and running a natural language processing pipeline.
Comparing production-grade NLP libraries: Training Spark-NLP and spaCy pipelines
A step-by-step guide to initialize the libraries, load the data, and train a tokenizer model using Spark-NLP and spaCy.
Big, fast, easy data with KSQL
A look at the new streaming SQL engine for Apache Kafka.
Rapid data production with a multi-model database
Ingest the data you need in an agile manner.
4 trends in security data science for 2018
A glimpse into what lies ahead for response automation, model compliance, and repeatable experiments.
When two trends fuse: PyTorch and recommender systems
A look at the rise of the deep learning library PyTorch and simultaneous advancements in recommender systems.
How self-service data avoids the dangers of “shadow analytics”
Without the proper cataloging, curation, and security that self-service data platforms allow, companies are left vulnerable to cybersecurity threats and misinformation.
What it means to be data-driven in the media industry
O’Reilly Media Podcast: David Hsieh, of Qubole, in conversation with John Slocum, of MediaMath.