The Deep Learning Toolkit (DLTK) was launched at .conf19 with the intention of helping customers leverage additional Deep Learning frameworks as part of their machine learning workflows. The app ships with four separate containers: Tensorflow 2.0 - CPU, Tensorflow 2.0 GPU, Pytorch and SpaCy. All of the containers provide a base install of Jupyter Lab & Tensorboard to help customers develop and create neural nets or custom algorithms.

Though much of the coverage of artificial intelligence or AI has been hype, the technology itself is real enough – and gaining traction in the commercial sphere. In fact, AI is increasingly being viewed as an integral requirement for business IT setups, rather than a luxury or fad. The research firm Gartner, Inc., predicts that more than 30 percent of data centers that fail to sufficiently prepare for AI will no longer be operationally or economically viable by 2020.

If you’re a tech geek you must have come across disrupting technologies like Artificial Intelligence, Machine Learning, Big Data, and IoT. These are the key buzz words since many years. With this blog we plan on kickstarting 2020 with the most sought-after question amongst IT administrators & DevOps team, “How will AI & ML benefit us?”, “What’s the role of AI in Networks?” & more. Well, let’s get started!

Unless you build or program computers for a living, you could be forgiven for never having wondered what actually happens under the hood. How do computers make decisions? What do their instructions look like? Can they, y’know… think?

The hype has gone off the charts for AI and machine learning tools' potential in IT organizations. It’s time to temper expectations and move towards a sensible adoption path. Artificial intelligence is not fairy dust sprinkled on a data center, despite the enthusiastic proclamations of many IT operations vendors today. A couple of years ago, AI was the bride atop the wedding cake. It was perfect, with promises to render obsolete errors and out-of-control performance issues.

The difference between machine learning for business and machine learning for consumers is that for business applications, you must deal with requirements that casual users won’t encounter.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore the relationship between AI and data privacy. From smart devices and voice assistants to mediating traffic and enhancing personalized shopping experiences, artificial intelligence (AI) has found widespread application in many aspects of life.

We recently launched Elastic Security, combining the threat hunting and analytics tools from Elastic SIEM with the prevention and response features of Elastic Endpoint Security. This combined solution focuses on detecting and flexibly responding to security threats, with machine learning providing core capabilities for real-time protections, detections, and interactive hunting. But why are machine learning tools so important in information security? How is machine learning being applied?

Augmented analytics is trending. You’ve read about it, you’ve heard about it, you may even be in the process of acquiring systems running it. But what exactly is it, and how can you recognize it? As the guys building augmented analytics, we’re here to dispel some of the hype. On the highest level, augmented analytics is the machine learning processes geared at making data more accessible and actionable for both data scientists and business users.

The digital era has brought vast cultural transformations – the sharing economy, microtransactions, lightning-fast communication and much more. Much of this has also resulted in considerable innovation in revenue-related areas. Companies from various industries today manage a large number of revenue streams from different revenue models.