 Big Data and Machine Learning Special Report |
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In this special newsletter we bring you up to date on all the new content and news related to Big Data and Machine Learning on InfoQ. |
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Enhancing Google Maps with Deep Learning and Street View (news, Jun 13, 2017) | Introducing Reladomo - Enterprise Open Source Java ORM, Batteries Included! (Part 2) (articles, Jun 13, 2017) | MongoDB Atlas Expands AWS Footprint (news, May 02, 2017) | Scio: Moving Big Data to Google Cloud, a Spotify Story (presentations, May 26, 2017) | Facebook Publishes New Neural Machine Translation Algorithm (news, May 24, 2017) |
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Top Viewed Content on InfoQ |
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Machine Learning Techniques for Predictive Maintenance (articles, May 21, 2017) | The InfoQ eMag: Introduction to Machine Learning (books, Apr 19, 2017) | Managing Data in Microservices (news, Jun 28, 2017) | Google Reveals Details of TensorFlow Processor Unit Architecture (news, Apr 24, 2017) | Data Preparation for Data Science: A Field Guide (presentations, Apr 23, 2017) |
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Apache Kafka is a distributed, fault-tolerant pub sub messaging soltuion, originally developed by LinkedIn and open sourced. Confluent was formed by former LinkedIn engineers in the Kafka development group and today announced Confluent Cloud, a fully hosted and managed Apache Kafka as a Service in AWS. We also take a look at Confluent's second annual Streaming Data report and its findings. | In a recent blog post, Google announced their Cloud Speech API has reached General Availability. The Cloud Speech API allows developers to include pre-trained machine learning models for cognitive tasks such as video, image and text analysis in addition to dynamic translation. The Cloud Speech API was launched, in open beta, last summer. |
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AWS has released Amazon DynamoDB Accelerator (DAX) in preview, a fully managed write-through caching service that sits logically in front of DynamoDB tables in order to improve performance for read-intensive workloads. DAX is API-compatible with DynamoDB, meaning that existing applications will not have to be re-written to take advantage of DAX. | Google recently announced the Cloud Machine Learning API updates at the Google Cloud Next Conference. This includes a set of APIs in the areas of vision, video intelligence, speech, natural language, translation and job search. | Over the past five years, Google searches for Machine Learning have gone up five times. “Fo anything that has machine learning in it or blockchain in it, the valuation goes up, 2, 3, 4, 5x”, Andy Stewart pointed out. Zachary Lipton claimed a "misinformation epidemic" in the field in a recent blog post. In this article we present the technical perspective of ML and how it can be presented. |
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FaunaDB Serverless Cloud is the managed version of FaunaDB, a serverless, object-relational, globally replicated, strongly consistent, temporal database, deployable on multiple clouds or on premises. |
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NoSQL databases are designed to store different types of data like Key Value, Documents, Time Series, Graph & IoT. Pascal Desmarets talks about how to do data modeling when using NoSQL databases. |
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In this article, the author discusses how to use Natural Language Processing (NLP) techniques to predict the movie ratings using the data shared on social media platforms. |
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InfoQ caught up with experts in the field to demystify the different topics surrounding AI, and how enterprise developers can leverage them today and thereby render their solutions more intelligently. |
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Deploy, operate, and scale a MongoDB database in the cloud with just a few clicks. Sponsored content |
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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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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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Stephen Whitworth talks about his experience at Ravelin, and provides useful practices and tips to help ensure our machine learning systems are robust, well audited, avoid embarrassing predictions. |
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Jonathan Ellis explains the challenges and successes Cassandra has had in creating transactions, materialized views, and a strongly consistent cluster membership within this peer-to-peer paradigm. |
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Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems. |
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