Given its wide applicability to real-world tasks, deep learning has attracted the attention of a wide audience of interested technologists, investors, and spectators. While the most celebrated results use feedforward convolutional neural networks (convnets) to solve problems in computer vision,...
This text is an attempt to recount some of the hard-earned lessons I have ended up learning, sometimes indirectly, but often personally. Everything is terrible, but our job still is to build something solid and usable on top of that everything. What we build adds to that 'everything', makes it bigger, more terrible.
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Textual data is a core source of information in the enterprise. Example demands arise from sales departments (monitor and identify leads), human resources (identify professionals with capabilities in ‘xyz’), market research (campaign monitoring from the social web), product development (incorporate feedback from customers), and the medical domain (anamnesis).
In this post, we describe In-Database Relation Extraction (INDREX), a system that transforms text data into relational data with Impala (the open source analytic database for Apache Hadoop), with low overhead needed for extraction, linking, and organization. Read this paper for complete details about our approach and our implementation.
Researchers at MIT have created a language, Picture, based on Julia that makes it much easier to write programs that use probabilistic reasoning for 2D and even 3D based computer vision. Their work is to be presented at the Computer Vision and Pattern Recognition conference in June.
Tachyon is a memory-centric distributed storage system enabling reliable data sharing at memory-speed across cluster frameworks, such as Spark and MapReduce. It achieves high performance by leveraging lineage information and using memory aggressively. Tachyon caches working set files in memory, thereby avoiding going to disk to load datasets that are frequently read. This enables different jobs/queries and frameworks to access cached files at memory speed.
Last week, I talked about how we set up Flashback to start benchmarking MongoDB 3.0 and highlighted the insane storage efficiencies achieved with both RocksDB and WiredTiger. As a recap, the amount of storage used dropped by more than 10x when importing a production replica set into either RocksDB or WiredTiger. This next post will dive into how MongoDB with RocksDB performs against production workloads. We initially focused on latencies, which were broken down by operation type. For comparison,
Here at VMware, we’ve recognize that containers, microservices, and DevOps – among other technologies and methodologies – are changing how modern applications are built, deployed, and managed. We’ve espoused our belief thatVMs and containers are better together, and we continue streamlining application development for DevOps teams on our unified platform. Our sister company, Pivotal, has been working on containers with us for several years, and both VMware and Pivotal continue to support open standards in the community.
Last year we announced QUIC, a UDP-based transport protocol for the modern Internet. Over the last quarter, we’ve been increasing the amount of traffic to Google services that is served over QUIC and analyzing QUIC performance at scale. Results so far are positive, with the data showing that QUIC provides a real performance improvement over TCP thanks to QUIC's lower-latency connection establishment, improved congestion control, and better loss recovery.
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