Strata 2016: Data preparation solutions are one of the major keys in adopting Big Data platforms on an enterprise level

| November 4, 2016

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For years and years business companies relied on proprietary products with sometimes expensive licenses and hardware to run on. These products ensured quality and reliability, while open-source software enjoyed a perception of being a sort of a playground or only good for a science project, not ready for production in the enterprise.

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Exabeam

Exabeam is the Smarter SIEM company. We empower enterprises to detect, investigate and respond to cyber attacks more efficiently so their security operations and insider threat teams can work smarter. Security organizations no longer have to live with excessive logging fees, missed distributed attacks and unknown threats, or manual investigations and remediation. With the Exabeam Security Management Platform, analysts can collect unlimited log data, use behavioral analytics to detect attacks, and automate incident response, both on-premises or in the cloud. Exabeam Smart Timelines, sequences of user and device behavior created using machine learning, further reduce the time and specialization required to detect attacker tactics, techniques and procedures

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AI and Predictive Analytics: Myth, Math, or Magic

Article | February 10, 2020

We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

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The importance of Big Data in the Food Industry Strategies and best practices

Article | March 5, 2020

Do you know the real importance of Big Data in the Food Industry? Knowing your audience is important, even fundamental for any kind of business. In this article we will analyze the best practices and the best data-driven strategies (marketing, but not only) for the food industry. Food and Beverage is a large and complex sector that embraces a number of very different players, some of whom are interconnected. The ecosystem includes both small producers and large multinational brands, players who cater to everyone and those who target a specific niche; then there are the distributors, clubs, restaurants both small and large, and retail chains.

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How can we democratize machine learning on IoT devices

Article | February 12, 2020

TinyML, as a concept, concerns the running of ML inference on Ultra Low-Power (ULP 1mW) microcontrollers found on IoT devices. Yet today, various challenges still limit the effective execution of TinyML in the embedded IoT world. As both a concept and community, it is still under development.Here at Ericsson, the focus of our TinyML as-a-Service (TinyMLaaS) activity is to democratize TinyML, enabling manufacturers to start their AI businesses using TinyML, which runs on 8, 16 and 32 bit microcontrollers.Our goal is to make the execution of ML tasks possible and easy in a specific class of devices. These devices are characterized by very constrained hardware and software resources such as sensor and actuator nodes based on these microcontrollers.Below, we present how we can bind the as-a-service model to TinyML. We will provide a high-level technical overview of our concept and introduce the design requirements and building blocks which characterize this emerging paradigm.

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Machine Learning vs. Deep Learning. Which Does Your Business Need?

Article | February 17, 2020

In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.

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Spotlight

Exabeam

Exabeam is the Smarter SIEM company. We empower enterprises to detect, investigate and respond to cyber attacks more efficiently so their security operations and insider threat teams can work smarter. Security organizations no longer have to live with excessive logging fees, missed distributed attacks and unknown threats, or manual investigations and remediation. With the Exabeam Security Management Platform, analysts can collect unlimited log data, use behavioral analytics to detect attacks, and automate incident response, both on-premises or in the cloud. Exabeam Smart Timelines, sequences of user and device behavior created using machine learning, further reduce the time and specialization required to detect attacker tactics, techniques and procedures

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