OmniSci has demonstrated the massive scaling possible using GPUs for computation and visualization. However, not every analytics problem or user persona requires massive scale; rather, our customers have expressed the desire to have proper-sized tools for the various problems they encounter across the enterprise. This talk will outline the vision for scaling the OmniSci platform from trillions of records in a giant data store to hundreds of millions of records on a laptop and every form factor in between. Whether you have a massive cluster of servers, a Data Science Workstation, a GPU-enabled laptop or even a CPU-only laptop, OmniSci can provide the same accelerated analytics experience appropriate for the problem at hand.

Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data? OmniSci’s GPU database technology lets us combine several factors into a suitability model considering roads and their relationships to terrain.

Learn how the OmniSci GPU-accelerated SQL engine fits into the overall RAPIDS partner ecosystem for open source GPU analytics. Using open data, OmniSci's Venkat Krishnamurthy, VP of Product Management and Aaron Williams, VP of Global Community, show how to ingest data that's from both streaming and standing sources, perform descriptive statistics and feature engineering using SQL and cuDF, and return the results as a GPU DataFrame. They also describe how data science workflow can be accomplished using tools from the RAPIDS ecosystem, all without the data ever leaving the GPU.

This webinar showcases OmniSci’s vision of pioneering modern hardware and software to allow for data insights at the speed of curiosity. One catalyst for this effort is through the open data science stack of today that is clearly a de-facto platform for experimentation. Through our shared vision and partnership with Intel, we are working to advance and unify the worlds of Data Science with traditional analytics on modern hardware. We will discuss the relationship between data science applications and data discovery along with the collaboration between OmniSci and Intel to advance innovation within this ecosystem.

Watch Wes McKinney provide insights into the development of open source over the past decade, open source’s contribution to the modern data science stack, and why community-led open source is at a critical point. Wes is the creator of Pandas, Ibis, author of Python for Data Analysis, co-founder and former CEO of DataPad, current Director of Ursa Labs, and member of the project management committee for Apache Arrow.

In this demo, we'll show you how to use OmniSci accelerated analytics, an ML ready platform to analyze and visualize billions of records of cybersecurity logs from one of our internal firewall.

In this session, we’ll discuss how a predictive algorithm helped the City of Flint focus their service line (water pipe) investigations in the areas at highest risk for having lead or galvanized steel service lines. We’ll discuss our work (in progress) to create a public map using best visual data and public health communication practices that, when completed, will allow Flint residents to visualize the predictive model outcomes and the pipe replacement progress in the city.

Data and analytics have played a central role in the first wave of the global response to COVID-19. From the earliest days of the pandemic, we’ve had up-to-date reports of case counts, fatalities and recoveries gathered by industrious volunteers everywhere. See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.

Telco markets worldwide are highly competitive. Consumers have more options than ever to satisfy their telecommunications needs. This means providers are competing harder, and paying closer attention to critical factors like cost of customer acquisition, average revenue per unit (ARPU), and customer churn. The smartest providers understand the tight relationship between these issues and their massive, and growing, stockpiles of data. Fortunately, there are new technologies that can handle billions of rows of data, joined across multiple datasets, with millisecond filtering and visualization times. With OmniSci, massive customer data is an asset instead of a barrier. In this webinar, you will learn: → How OmniSci can query billions of rows of data, create interactive dashboards, literally at the speed of your curiosity → With OmnSci you can bring multiple cohorts of data together in one easy to interpret geospatial display → How this analysis capability, along with AI and ML, can unlock insights previously hidden, and allow us to predict the future and drive mitigation before it affects your networks or your customers.

The unique benefits of 5G is expected to unlock $1.4tn of value in eight key industries by 2030. This is based on models generated from 200+ interviews and surveys with industry executives. What steps should operators take to deliver these benefits? What business and organisational transformations must occur to unlock this opportunity? 1) As we’ve seen earlier, the big data revolution has helped operators design-in resiliency , but it has also made it necessary for business leaders to invest in technologies that can handle the scale and performance of their largest datasets. Only decision makers with the best and most informed understanding of their data can set the standard for their business’s success. What are still the biggest barriers to leveraging your data? Where are your current tools struggling to meet your expectations? 2) “We simply can’t get hold of the data” Are data rules and regulations hampering CSPs usage of data? What is the opportunity to leverage the telecom data for your B2B business? 3) We’ve seen a sharp rise in operators adding predictive analytics, using AI and ML, into their data strategy to reduce costs, make faster, better decisions, and create new products or services to meet customers’ changing needs. Where is your organization in the adoption of predictive analytics and what kinds of benefits have you seen from leveraging the technology?