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Top Databases Supporting in-Database Machine Learning - ELE Times

#artificialintelligence

In my August 2020 article, "How to choose a cloud Machine Learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.


8 databases supporting in-database machine learning

#artificialintelligence

In my August 2020 article, "How to choose a cloud machine learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.


Advanced In-Database Analytics on the GPU - Kinetica

#artificialintelligence

With Version 6.0, Kinetica introduces user-defined functions (UDFs), enabling GPU-accelerated data science logic to power advanced business analytics, on a single database platform. User-defined functions (UDFs) enable compute as well as data-processing, within the database. Such'in-database processing' is available on several high-end databases such as Oracle, Teradata, Vertica and others, but this is the first time such functionality has been made available on a database that fully utilizes the parallel compute power of the GPU on a distributed platform. In-database processing in Kinetica creates a highly flexible means of doing advanced compute-to-grid analytics. This industry-first functionality stands to help democratize data science.


Oracle Database 21c spotlights in-memory processing and ML, adds new low-code APEX cloud service

ZDNet

Among the messages that Oracle is putting out for its flagship database, adding new access paths for developers has become just as important as adding new data types. This month, Oracle is launching the next version of Oracle Database, version 21c. In a session hosted by Andrew Mendelsohn, executive vice president of database server technologies, the company is also announcing a new cloud-based APEX Service designed to carve a new access path for low-code developers who traditionally thought that writing apps for Oracle was complex and expensive. To induce new developers, Oracle is throwing in a free tier to this new cloud service. As Oracle now numbers its releases according to calendar year, 21c is the next release, which was announced as generally available last month.


Data 2021 Outlook Part I: What's ahead for AI and Cloud Data Warehousing

ZDNet

If there's one obvious prediction that bore out over the course of what was otherwise a very unpredictable year, it was the acceleration in adoption of cloud computing. Just look at the continued very healthy double digit growth rates of each of the major clouds. For enterprises, it was about adapting to the virtual environment and constrained supply chains of a suddenly locked-down world. A year ago, (pre-COVID), we viewed cloud adoption as a series of logical stages, evolving from DevTest to development of new born-in-the-cloud apps, opportunistic adoption of new SaaS services, with the homestretch now coming into view with replatforming and/or transformation of core enterprise back end applications. But with hindsight, nor surprisingly, the headline for cloud adoption over the past year was for use cases enabling businesses to pivot into what became the new normal – the need to change or develop new services in a landscape where work and consumption was increasingly virtual, and where conventional supply chains came under stress.