28

Jun

Get video insights in (even) more languages!

For those of you who might not have tried it yet, Video Indexer is a cloud application and platform built upon media AI technologies to make it easier to extract insights from video and audio files. As a starting point for extracting the textual part of the insights, the solution creates a transcript based on the speech appearing in the file; this process is referred to as Speech-to-text. Today, Video Indexer’s Speech-to-text supports ten different languages. Supported languages include English, Spanish, French, German, Italian, Chinese (Simplified), Portuguese (Brazilian), Japanese, Arabic, and Russian.

However, if the content you need is not in one of the above languages, fear not! Video Indexer partners with other transcription service providers to extend its speech-to-text capabilities to many more languages. One of those partnerships is with Zoom Media, which extended the Speech-to-text to Dutch, Danish, Norwegian and Swedish.

A great example for using Video Indexer and Zoom Media is the Dutch public broadcaster AVROTROS; who uses Video Indexer to analyze videos and allow editors to search through them. Finus Tromp, Head of Interactive Media in AVROTROS shared, “We use Microsoft Video Indexer on a daily basis to supply our videos with relevant metadata. The gathered

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28

Jun

How tech is moving the insurance industry to be more customer-focused

Insurers need to become the home page for their customers. A home page is a source of meaningful, targeted and useful content that the customer seeks out. Technology can now deliver innovative solutions directly to insurance customers. Artificial Intelligence (AI) and Machine Learning (ML) are delivering targeted content continually from multiple sources, including data generated from IoT devices owned by customers, in real time. Focusing on the customer will make the relationship more valuable to the consumer, and will increase the number of interactions with the insurance company. Technology is making that happen from the point of first interaction. For example, when a potential customer interacts with a company’s website, or with an app powered by bots. Then the company uses AI to make sure the products are correctly matched and priced for the customer. As the needs of the customer changes, the enterprise delivers continuing, direct support.

A survey by InsuranceNexus in June 2018, titled Insurance Customer Engagement Europe, 400 senior insurance executives were surveyed, 91 percent agreed that the insurance industry must become more customer-centric. With that, Bain reports that insurance companies can boost revenues, improve margins and most importantly, sustain the loyalty of their very demanding customers.

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28

Jun

Autocomplete in Azure Search now in public preview

Today, we are happy to announce public preview support for autocomplete in Azure Search, one of our most requested features on UserVoice. Autocomplete, also called “type-ahead search”, can enhance the search experience by finding potential terms in the index that match the partial term being written by the user. An example of similar functionality is shown below from bing.com:

 

Autocomplete modes

For autocomplete to work, the fields in the search index should be listed in a suggester. For example, if you define a suggester on a city field, typing “sea” will return terms like “Seattle” or “Seaside” if those terms exist in the city field.

The table below describes the 3 modes supported by Autocomplete and the expected results in each scenario.

Mode Behavior Query Autocompleted results OneTerm Returns a single term which autocompletes the partial term of the query. “what a wo

“women”

“wool”

“world”

TwoTerms Returns a single term and two terms which autocomplete the partial term of the query. “what a wo

“wonderful day”

“wolf spider”

“woman”

“world”

OneTermWithContext Returns two terms such that the last two terms from the input query appear together. “what a wonderful d

“wonderful day”

“wonderful dog”

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28

Jun

Autocomplete in Azure Search now in public preview

Today, we are happy to announce public preview support for autocomplete in Azure Search, one of our most requested features on UserVoice. Autocomplete, also called “type-ahead search”, can enhance the search experience by finding potential terms in the index that match the partial term being written by the user. An example of similar functionality is shown below from bing.com:

 

Autocomplete modes

For autocomplete to work, the fields in the search index should be listed in a suggester. For example, if you define a suggester on a city field, typing “sea” will return terms like “Seattle” or “Seaside” if those terms exist in the city field.

The table below describes the 3 modes supported by Autocomplete and the expected results in each scenario.

Mode Behavior Query Autocompleted results OneTerm Returns a single term which autocompletes the partial term of the query. “what a wo

“women”

“wool”

“world”

TwoTerms Returns a single term and two terms which autocomplete the partial term of the query. “what a wo

“wonderful day”

“wolf spider”

“woman”

“world”

OneTermWithContext Returns two terms such that the last two terms from the input query appear together. “what a wonderful d

“wonderful day”

“wonderful dog”

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28

Jun

Augmented Reality becomes mainstream in Manufacturing, changes the face of the industry

As it’s often the case with new technologies, Augmented Reality (and its more evolved sibling – Mixed Reality)  have attracted a lot of excitement in the tech world and sparked the imaginations of many entrepreneurs. This excitement is easy to understand. This is, after all, a technology that lets us blend the digital with the physical and enhance our senses with super-human capabilities.

However, the value of AR/MR was not immediately obvious to everyone in Manufacturing (we are a skeptical bunch). Early examples of AR/MR applications did not help its case: they showed a group of engineers gathered around a 3D model, waving their hands in the air to virtually pull parts apart and mark things up with “please review this part” virtual notes. The problem with this was that these examples were essentially the same scenarios already beaten to death by VR, with the only apparent difference that engineers used see-through glasses instead of opaque head-mounted devices. So, naturally, people wondered what the big whoop around AR/MR was.

But soon enough folks in the industry realized that the real value of AR/MR was in augmenting the real world with digital information, and not just projecting 3D models in physical

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28

Jun

Augmented Reality becomes mainstream in Manufacturing, changes the face of the industry

As it’s often the case with new technologies, Augmented Reality (and its more evolved sibling – Mixed Reality)  have attracted a lot of excitement in the tech world and sparked the imaginations of many entrepreneurs. This excitement is easy to understand. This is, after all, a technology that lets us blend the digital with the physical and enhance our senses with super-human capabilities.

However, the value of AR/MR was not immediately obvious to everyone in Manufacturing (we are a skeptical bunch). Early examples of AR/MR applications did not help its case: they showed a group of engineers gathered around a 3D model, waving their hands in the air to virtually pull parts apart and mark things up with “please review this part” virtual notes. The problem with this was that these examples were essentially the same scenarios already beaten to death by VR, with the only apparent difference that engineers used see-through glasses instead of opaque head-mounted devices. So, naturally, people wondered what the big whoop around AR/MR was.

But soon enough folks in the industry realized that the real value of AR/MR was in augmenting the real world with digital information, and not just projecting 3D models in physical

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28

Jun

A closer look at Azure Data Lake Storage Gen2

On June 27th, 2018 we announced the preview of Azure Data Lake Storage Gen2 the only data lake designed specifically for enterprises to run large scale analytics workloads in the cloud. Azure Data Lake Storage Gen2 takes core capabilities from Azure Data Lake Storage Gen1 such as a Hadoop compatible file system, Azure Active Directory and POSIX based ACLs and integrates them into Azure Blob Storage. This combination enables best in class analytics performance along with Blob Storage’s tiering and data lifecycle management capabilities and the fundamental availability, security and durability capabilities of Azure Storage.

In this blog post, we are going to drill into why Azure Data Lake Storage Gen2 is unique. Taking a closer look at the innovative Hadoop file system implementation, Azure Blob Storage integration and a quick review of why Azure Data Lake Storage Gen2 enables the lowest total cost of ownership in the cloud.

High-Fidelity server side Hadoop file system with hierarchical namespace

The simplest way to enable Hadoop applications to work with cloud storage is to build a Hadoop File System driver which runs client-side within the Hadoop applications as illustrated in diagram 1. This driver emulates a file system, converting Hadoop file system

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28

Jun

A closer look at Azure Data Lake Storage Gen2

On June 27, 2018 we announced the preview of Azure Data Lake Storage Gen2 the only data lake designed specifically for enterprises to run large scale analytics workloads in the cloud. Azure Data Lake Storage Gen2 takes core capabilities from Azure Data Lake Storage Gen1 such as a Hadoop compatible file system, Azure Active Directory and POSIX based ACLs and integrates them into Azure Blob Storage. This combination enables best in class analytics performance along with Blob Storage’s tiering and data lifecycle management capabilities and the fundamental availability, security and durability capabilities of Azure Storage.

In this blog post, we are going to drill into why Azure Data Lake Storage Gen2 is unique. Taking a closer look at the innovative Hadoop file system implementation, Azure Blob Storage integration and a quick review of why Azure Data Lake Storage Gen2 enables the lowest total cost of ownership in the cloud.

High-Fidelity server side Hadoop file system with hierarchical namespace

The simplest way to enable Hadoop applications to work with cloud storage is to build a Hadoop File System driver which runs client-side within the Hadoop applications as illustrated in diagram 1. This driver emulates a file system, converting Hadoop file system

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27

Jun

Automatic device management, module identity, and module twin are now generally available

Last month, we released Azure IoT Hub automatic device management, and module identity and module twins. Each of these features enable scenarios to enhance device management capabilities within your IoT application built on Azure IoT Hub. Today, we are excited to announce that they are generally available with the same great support you’ve come to know and expect from Azure IoT services.

Automatic device management

Automatic device management automates many of the repetitive and complex tasks of managing large device fleets over the entirety of their lifecycles. With automatic device management, you can target a set of devices based on their properties, define a desired configuration, and let IoT Hub update devices whenever they come into scope. We offer two services in automatic device management for different scenarios – automatic device configurations and IoT Edge automatic deployments.

Automatic device configurations

Automatic device configurations provides the ability to perform IoT device configuration at scale including updating settings, installing software, and updating firmware with reporting and conflict resolution automatically handled. It provides an additional layer of capability by building upon existing platform primitives, namely device twins, and queries.

With general availability support comes expanded SDK support. We now support service SDKs

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27

Jun

Automatic device management, module identity, and module twin are now generally available

Last month, we released Azure IoT Hub automatic device management, and module identity and module twins. Each of these features enable scenarios to enhance device management capabilities within your IoT application built on Azure IoT Hub. Today, we are excited to announce that they are generally available with the same great support you’ve come to know and expect from Azure IoT services.

Automatic device management

Automatic device management automates many of the repetitive and complex tasks of managing large device fleets over the entirety of their lifecycles. With automatic device management, you can target a set of devices based on their properties, define a desired configuration, and let IoT Hub update devices whenever they come into scope. We offer two services in automatic device management for different scenarios – automatic device configurations and IoT Edge automatic deployments.

Automatic device configurations

Automatic device configurations provides the ability to perform IoT device configuration at scale including updating settings, installing software, and updating firmware with reporting and conflict resolution automatically handled. It provides an additional layer of capability by building upon existing platform primitives, namely device twins, and queries.

With general availability support comes expanded SDK support. We now support service SDKs

Share