AI News, MemSQL 6 Product Pillars and Machine Learning Approach

MemSQL 6 Product Pillars and Machine Learning Approach

MemSQL 6 closes the gap between machine learning and operational applications in three areas: Built-in machine learning functions Real-time machine learning scoring Machine learning in SQL with extensibility SEPTEMBER 26, 2017

This release encapsulates over one year of extensive development to continue making MemSQL the best database platform for real-time analytics with a focus on real-time data warehouse use cases.

Benefits of extensibility include the ability to centralized processes in the database across multiple applications, the performance of embedded functions, and the potential to create new machine learning functions as detailed later in this post.

MemSQL 6 includes dictionary encoding, which can translate data into highly compressed unique values that can then be used to conduct incredibly fast scans.

Machine Learning and MemSQL 6 MemSQL 6 helps close the gap between machine learning and operational applications in three areas: Built-in machine learning functions Real-time machine learning scoring Machine learning in SQL with extensibility Built-in Machine Learning Functions MemSQL 6 includes new machine learning functions like DOT_PRODUCT, which can be used for real-time image recognition but also for any application requiring the comparison of two vectors.

While this function itself is not new in the world of machine learning, MemSQL now delivers this function within its distributed SQL database, enabling an unprecedented level of performance and scale.

This can dramatically shorten the gap between data science and production applications as operations occur on the live data, and models can be trained and updated to incorporate and reflect the most recent data.

distributed, scale out architecture well suited to performance and large scale workloads An open source MemSQL Spark Connector for high-throughput, highly-parallel, and bidirectional connectivity to Spark Native integration with Kafka message queues including the ability to support exactly-once semantics Full transactional SQL semantics so you can build production applications for the front lines of your business Together, we see these capabilities as foundational for real-time machine learning workloads, and we invite you to try the latest version of MemSQL today atmemsql.com/download.

MemSQL Takes Machine Learning Models Real-Time

NEW YORK CITY, NY - September 26, 2017 - MemSQL, provider of the fastest real-time data warehouse, today showcased at the Strata Data Conference how it is closing the machine learning (ML) gap between data science and operational applications.

“MemSQL 6 now has capabilities such as extensibility to enable more ML functions, so customers can meet the needs of their most ambitious challenges.” According to a recent report, IDC Research forecasts worldwide revenues for cognitive and artificial intelligence systems will reach $12.5 billion in 2017.

Connect with MemSQL at Strata Data Conference in New York September 26-28, stop by MemSQL Booth #423 for live demonstrations or attend one of the following talks: Teaching Databases to Learn in the World of AI Nikita Shamgunov, CEO and co-founder, MemSQL 9:45am–9:50am, Wednesday, September 27, 2017 Location: 3E Building a Real-Time Feedback Loop for Education David Mellor, VP and chief architect, Curriculum Associates 11:20am–12:00pm, Wednesday, September 27, 2017 Location: 1A 04/05 Exploring Real-Time Capabilities with Spark SQL Lucy Yu, software engineer, MemSQL 2:05pm–2:45pm, Wednesday, September 27, 2017 Location: 1A 21/22 About MemSQL MemSQL envisions a world of adaptable databases and flexible data workloads - your data anywhere in real time.

MemSQL 6: Product Pillars and Machine Learning Approach

This release encapsulates over one year of extensive development to continue making MemSQL the best database platform for real-time analytics with a focus on real-time data warehouse use cases.

Benefits of extensibility include the ability to centralized processes in the database across multiple applications, the performance of embedded functions, and the potential to create new machine learning functions as detailed later in this post.

MemSQL 6 includes dictionary encoding, which can translate data into highly compressed unique values that can then be used to conduct incredibly fast scans.

Machine Learning and MemSQL 6 MemSQL 6 helps close the gap between machine learning and operational applications in three areas: Built-in machine learning functions Real-time machine learning scoring Machine learning in SQL with extensibility Built-In Machine Learning Functions MemSQL 6 includes new machine learning functions like DOT_PRODUCT, which can be used for real-time image recognition but also for any application requiring the comparison of two vectors.

While this function itself is not new in the world of machine learning, MemSQL now delivers this function within its distributed SQL database, enabling an unprecedented level of performance and scale.

This can dramatically shorten the gap between data science and production applications as operations occur on the live data, and models can be trained and updated to incorporate and reflect the most recent data.

distributed, scale-out architecture well-suited to performance and large-scale workloads An open-source MemSQL Spark Connector for high-throughput, highly-parallel, and bidirectional connectivity to Spark Native integration with Kafka message queues including the ability to support exactly-once semantics Full transactional SQL semantics so you can build production applications for the front lines of your business Together, we see these capabilities as foundational for real-time machine learning workloads, and we invite you to try the latest version of MemSQL today here.

MemSQL Adds Machine Learning Models to Flagship Platform

Source: MemSQL on Twitter MemSQL announced version 6 of their flagship data warehousing platform at Strata Data Conference last week.

MemSQL 6 includes the ability to run ML algorithms in a distributed SQL environment, enhancements to online operations, and increases to query performance to deliver up to “80 times improvement” from previous versions, according to the provider.

For organizations wanting to bring machine learning functions closer to live data, version 6 supports real-time scoring with existing or new models, DOT_PRODUCT for image recognition, and new extensibility capabilities that enable functions, such as K-means in SQL.

The platform utilizes modern chip architectures including Single Instruction, Multiple Data for performance and can fire as high as processing a billion rows per CPU core per second.

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