Modern Data Stack (MDS) is a stack of technologies that makes a modern data warehouse perform 10–10,000x better than a legacy data warehouse. Ultimately, an MDS saves time, money, and effort. The four pillars of an MDS are a data connector, a cloud data warehouse, a data transformer, and a BI & data exploration tool. Easy integration is made possible with managed and open-source tools that pre-build hundreds of ready-to-use connectors. What used to take a team of data engineers to build and maintain regularly can now be replaced with a tool for simple use cases.

Israel has sought to increase its operational success on the battlefield through a major push for digitization in the Israel Defense Forces. The importance of this transformation was apparent in the recent conflict in Gaza that Israeli officials have called the first "artificial intelligence war." Chief of Staff Aviv Kochavi has made employing digital potential a central feature of his command, according to Col. Eli Birenbaum, head of the IDF Digital Transformation Division's Architecture Department. "The IDF had a few shortcomings to increase our lethality on the battlefield," said Birenbaum in an interview. While the IDF looks like one organization from the outside, for years its different services, including the air force, navy and ground forces, were balkanized in their use of their own networks for data services, he said.

Natural Language Processing (NLP) will enable better understanding all around: we'll talk to our computers; our computers will understand us; and we'll have the Star Trek Universal Communicator in our ears translating any language into our native language in real time (and vice versa). Before we get to long, philosophical, and emotional natural conversations with our computers (as in the movie Her, we can build a lot of extremely useful language-enabled applications that help do things like understand whether someone is getting angry on a support call, write better job descriptions, and disambiguating words whose meaning change depending on context (see this Wikipedia page for a fun list of examples including one of my favorite perfectly grammatical sentences: Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. Scroll down this page to see modern AI services in action figuring out the emotional tilt of a sentence, translating English into Chinese, and more. Let's explore a few of these capabilities by calling real-world APIs, some from the open source community and others from the major public cloud providers such as Google, Microsoft, and IBM. Check the"I'm not a robot" box, and hit analyze.

I've recently developed a strong interest for performance monitoring. This is the opportunity for me to expand my skills on Application Insights, which I've been using for many years, but also complement them with an overview of all the monitoring services available on Azure. As often on Azure, these services provide similar and overlapping features and even though this page gives helpful examples of "when to use which", I thought I would outline here, and in my own words, a review of their features, differences and similarities. Infrastructure and application monitoring are very different tasks that require different metrics and different exploitation of those metrics. Infrastructure monitoring calls for both a detailed reporting of the activity of each service of your system, and a synthetic, high-level overview of the global health of that system.

When the news broke last month that Microsoft was to acquire LinkedIn for a little over 26 billion, it sent shockwaves through the tech world as one of the most expensive deals of its kind in history. The question is: Why did it happen, what was the potential gain for each side of the table and where do we go from here? The truth is, we've seen a number of acquisitions like these, even if the particular deals we've witnessed haven't reached 26 billion. In this case, it appears that both Microsoft and LinkedIn needed each other in a way that would defend their position and possibly grow much faster. Microsoft wants to integrate LinkedIn's database into a variety of Microsoft products for greater intelligence so that Skype, Word or Exchange will be LinkedIn enabled.

Microsoft wants to join the rest of the software giants in the AI game, but it just keeps striking out. Weeks ago the firm's lovable teen chatbot turned into a Hitler supporting racists and this week its CaptionBot isn't living up to its potential. CaptionBot, which analyzes pictures in order to formulate captions, has been spot on with some results, but horridly wrong for others – it thought the First Lady Michelle Obama was a cell phone. Weeks ago the firm's lovable teen chatbot turned into a Hitler supporting racists and this week its CaptionBot isn't living up to its name. This technology combines three different services to process images and write descriptive captions – and sometimes includes an appropriate emoji.