InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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In Depth TensorFlow
Illia Polosukhin keynotes on TensorFlow, introducing it and presenting the components and concepts it is built upon.
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Blockchain: The Oracle Problems
Paul Sztorc talks about why the oracle problem is so hard (the historical evolution of failures, why they fail), and the basics of blockchain ("blockchain as immortal software", ledger "rents").
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Practical Blockchains: Building on Bitcoin
Peter Todd answers the questions: why use Bitcoin over other blockchains, what is safe, future proof ways to peg data to Bitcoin's blockchain and what is Bitcoin script, and how it can be used.
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Comparing Deep Learning Frameworks
Jeffrey Shomaker covers the different types of deep learning frameworks and then focuses on neural networks, including business uses and 4 of the main systems (eg. Tensor Flow) that are open sourced.
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AI-Based Data Extraction
George Roth presents the challenges of data extraction from unstructured content in the context of preparing the data for Data Analytics.
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Scio: Moving Big Data to Google Cloud, a Spotify Story
Neville Li tells the Spotify’s story of migrating their big data infrastructure to Google Cloud, replacing Hive and Scalding with BigQuery and Scio, which helped them iterate faster.
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Deep Learning Applications in Business
Diego Klabjan discusses models, implementations, and challenges developing applications for trading, forecasting, and healthcare, detailing relevant models and issues adopting and deploying them.
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Beyond Chatbots: The Future of AI and Business
Will Murphy explores chatbots, the use of AI and what’s in store for businesses using them in the future.
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Data Preparation for Data Science: A Field Guide
Casey Stella presents a utility written with Apache Spark to automate data preparation, discovering missing values, values with skewed distributions and discovering likely errors within data.
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AI from an Investment Perspective
The panelists discuss AI from an investment perspective, the challenges, the risks, trends, the role of Deep Learning, successful AI use cases, and more.
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Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
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Straggler Free Data Processing in Cloud Dataflow
Eugene Kirpichov describes the theory and practice behind Cloud Dataflow's approach to straggler elimination, and the associated non-obvious challenges, benefits, and implications of the technique.
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