InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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The State of AI
Jim McHugh keynotes on the current state of artificial intelligence.
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Data Driven Products Now!
Dan McKinley discusses how Etsy is using data to validate their ideas and prototypes, turning some into real products.
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ScyllaDB: Achieving No-Compromise Performance
Avi Kivity discusses ScyllaDB, the many necessary design decisions, from the programming language and programming model through low-level details and up to the advanced cache design, and more.
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Fundamentals of Stream Processing with Apache Beam
Frances Perry and Tyler Akidau discuss Apache Beam, out-of-order stream processing, and how Beam’s tools for reasoning simplify complex tasks.
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Data Science in the Cloud @StitchFix
Stefan Krawczyk discusses how StitchFix used the cloud to enable over 80 data scientists to be productive and have easy access, covering prototyping, algorithms used, keeping schema in sync, etc.
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Elastic Data Analytics Platform @Datadog
Doug Daniels discusses the cloud-based platform they have built at DataDog and how it differs from a traditional datacenter-based analytics stack, pros and cons and the tooling built.
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Petabytes Scale Analytics Infrastructure @Netflix
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
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Big Data in the Real World: Technology and Use Cases
Mike Olson presents several use cases where big data is collected and analyzed to gather insights from the automotive, insurance, financial, and other sectors.
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Using Bayesian Optimization to Tune Machine Learning Models
Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.
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Machine Learning and End-to-End Data Analysis Processes in Spark Using Python and R
Debraj GuhaThakurta discusses ML and data analysis processes in Spark using examples written in Python and R.
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Machine Learning Your Way to Smarter API Error Responses
Steven Cooper discusses using machine learning to understand malformed API requests to not only respond with a best fit response, but capture the user errors for future responses.
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I Can't Believe It's Not a Queue: Using Kafka with Spring
Joe Kutner talks about Kafka and where it fits in a Spring app and how to make it do things message queues simply can't.
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