HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities

 HEAVY 7.0 supercharges analytics for telcos and utilities with groundbreaking predictive modeling features, improved HEAVY Immerse experience, and enhanced HeavyRF operational configuration

HEAVY.AI, an innovator in advanced analytics, announced general availability of HEAVY 7.0. The new product adds innovative machine learning capabilities, enabling telcos and utilities to perform in-database predictive modeling and simulate any scenario to uncover key insights. HEAVY 7.0 also incorporates new ways to interactively join and fuse data in the Heavy Immerse visualization platform, as well as more powerful cell site planning and optimization capabilities via significant enhancements to the HeavyRF telco module.

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“For telcos and utilities, delivering the best service to their customers means constantly analyzing, investigating and learning from the immense amounts and vast sources of data available to them. But analyzing complex geospatial data combined with customer and radiofrequency data, is a cumbersome and error-prone process”

“For telcos and utilities, delivering the best service to their customers means constantly analyzing, investigating and learning from the immense amounts and vast sources of data available to them. But analyzing complex geospatial data combined with customer and radiofrequency data, is a cumbersome and error-prone process,” said Jon Kondo, CEO, HEAVY.AI. “HEAVY 7.0 provides tools and features that make it fast and easy for these organizations to analyze any type of data and uncover insights that are critical for their business.”

Introduction of machine learning capabilities via predictive modeling in-database

HEAVY 7.0 introduces HeavyML, enabling predictive analytics directly in-database as a public beta feature. Implemented as native SQL operators that can be evaluated interactively on GPUs and then visualized and rendered in Heavy Immerse dashboards, HeavyML supports a variety of clustering and regression algorithms, including tree-based models such as random forest regression. With this addition, domain experts and other end users not intimately familiar with data science workflows can leverage predictive analytics on large datasets.

Expanded HeavyRF Cell Site Planning and Optimization Capabilities

HEAVY 7.0 features a new site editor for graphical specification of network hardware and its configuration under various, complex operating scenarios, such as rush hour scenarios, reduced power or maintenance modes and seasonal or monthly variation in vegetation optical thickness – a critical capability for midband 5G and lower frequencies. As a result, telcos can develop and test various software optimized network (SON) scenarios safely and with full visibility to potential customer experience impacts.

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HeavyRF has also gained improved workflows for establishing and monitoring business targets involving large numbers of buildings. HeavyRF has always provided a continuously updated view of relevant business metrics, but now it can target thousands of buildings at once using fully customizable and extensible building tagging.

The addition of no-code joins in Heavy Immerse

Heavy Immerse has long offered non-technical users the ability to rapidly visualize, map and filter enormous datasets interactively and in-real-time. Heavy 7.0 further enables users to get rapid, visual insights from their data via the addition of no-code join capabilities. Joins can now be specified directly from Heavy Immerse dashboards, and thanks to the speed of the underlying GPU database, execute across multi-billion record datasets at interactive speeds, no indexing or down-sampling required. The ability to performantly fuse large datasets without writing SQL further democratizes access to complex insights for a broad set of users.

A major California utility is using the new join capabilities to downscale huge weather models, measuring weather’s impact on specific assets as well as their effect on customers.

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