InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Forecasting Using Data - Quickly Answering How Big, How Long and How Likely
Troy Magennis explains in this workshop how to capture data and use it for reliable project forecasting using a practical and simple approach to forecasting without item effort estimation.
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Impact of Machine Learning Systems in Industries
The panelists discuss the impact machine learning is having on various industries.
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But I Need a Database that _Scales_
Aaron Spiegel reviews common scaling techniques for both relational and NoSQL databases, discussing trade-offs of these techniques and their effect on query flexibility, transactions and consistency.
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Cassandra and DataStax Enterprise on PCF
Ben Lackey and Cornelia Davis start with the use cases for on-demand, dedicated DSE clusters, cover the solution design, and demo the system, touching also the support that Spring has for Cassandra.
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ETL Is Dead, Long Live Streams
Neha Narkhede shares the experience at LinkedIn moving from ETL to real-time streams, the challenges of scaling Kafka to hundreds of billions of events/day, supporting thousands of engineers, etc.
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MLeap: Release Spark ML Models
Hollin Wilkins discusses the reasons behind MLeap, outes the programming time saved by using it, shows benchmarks of several online models, and provides a demo and examples of using it in practice.
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Elasticsearch for SQL Users
Shaunak Kashyap looks at several well-understood concepts and SQL queries from the relational paradigm and maps these to their Elasticsearch equivalents.
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The History and Future of Wearable Computing and Virtual Experience
Amber Case talks about the road from VR to AR, the history and future of wearables, human augmentation, infrastructure, machine vision, computer backpacks, heads up displays, reality editing, etc.
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MongoDB Aggregation - Going Way beyond the Query
Nuri Halperin discusses the aggregation framework in MongoDB, explaining the pipeline architecture, major operators, and how to put it all together in interesting and effective ways.
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Validation Methodology of Large Unstructured Unsupervised Learning Systems
Lawrence Chernin describes best practices and validation methods used to deal with large unstructured data, including a suite of unit tests covering the implementations of algorithmic equations.
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Machine Learning Exposed!
James Weaver takes a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning, surveying various machine learning APIs and platforms.
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How Predictive Analytics Boosts the Customer Experience at the Georgia Aquarium
Beach Clark talks about the technological and cultural challenges of turning data science into a vital part of the business model at Georgia Aquarium.
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