Category Archives : Artificial Intelligence

28

Sep

What to expect in Spark + AI summit Europe
What to expect in Spark + AI summit Europe

The Spark + AI summit Europe kicks-off in just a few days in London. Microsoft and many of their customers using Azure Databricks are present during the Summit. Azure Databricks is a first party service on Azure, allowing customers to accelerate big data analytics and artificial intelligence (AI) solutions with a fast, easy, and collaborative Apache SparkTM–based analytics service. Having such a platform improves developer productivity with a single, consistent set of APIs and developers can mix and match different kinds of processing within the same environment. Azure Databricks also improves performance by eliminating unnecessary movement of data across environments.

Here are a few recommended sessions you might find interesting, where customers and partners share success stories leveraging Azure Databricks:

For Oil & Gas Moving Towards AI: Learn from an actual customer how they are leveraging deep learning with Azure Databricks to implement a solution that enables them to detect safety incidents at their gas stations. Also learn how they were able to build an Advanced Analytics COE to lead AI projects across the organization. For Retail Co-op’s Transformation from Brick and Mortar to AI with Databricks: In this session, learn from the head of data within a consumer co-operative

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24

Sep

Experimentation using Azure Machine Learning
Experimentation using Azure Machine Learning

We are pleased to announce public preview refresh of the Azure Machine Learning (AML) service. The refresh contains many new improvements that increase the productivity of data scientists.

In this post, I want to highlight some of the improvements we made around machine learning experimentation, which is the process of developing, training, and optimizing a machine learning model. Experimentation also often includes auditing, governing, sharing, repeating, understanding and other enterprise-level functions. Read more about this high-level overview of the Azure Machine Learning service strategy and direction.

Machine Learning experimentation

The process of developing machine learning models for production involves many steps. First, the data scientist must decide on a model architecture and data featurization.  Next, they must train and attempt to tune these models. This requires them to manage the compute resources to execute and scale out training, collect the training data and make it available to the target compute resource. They also must keep track of the different (hyper-)parameter combinations and model versions used along the way, and which results they yielded. All that is often embedded in a complex data flow needed to acquire and prepare the data on the preprocessing side and to post-process and deploy the

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24

Sep

What’s new in Azure Machine Learning service
What’s new in Azure Machine Learning service

Today we are very happy to release the new capabilities for the Azure Machine Learning service. Since our initial public preview launch in September 2017, we have received an incredible amount of valuable and constructive feedback. Over the last 12 months, the team has been very busy enhancing the product, addressing feedbacks, and adding new capabilities. It is extremely exciting to share these new improvements with you. We are confident that they will dramatically boost productivity for data scientists and machine learning practitioners in building and deploying machine learning solutions at cloud scale.

In this post, I want to highlight some of the core capabilities of the release with a bit more technical details.

Python SDK & Machine Learning workspace

Most of the recent machine learning innovations are happening in the Python language space, which is why we chose to expose the core features of the service through a Python SDK. You can install it with a simple pip install command, preferably in an isolated conda virtual environment.

# install just the base package $ pip install azureml-sdk # or install additional capabilities such as automated machine learning $ pip install azureml-sdk[automl]

You will need access to an Azure

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24

Sep

Azure AI – Making AI real for business

AI, data and cloud are ushering the next wave of transformative innovations across industries. With Azure AI, our goal is to empower organizations to apply AI across the spectrum of their business to engage customers, empower employees, optimize operations and transform products. We see customers using Azure AI to derive tangible benefits across three key solution areas. 

First, using machine learning to build predictive models that optimize business processes. Second, building AI powered apps and agents to deliver natural user experience by integrating vision, speech and language capabilities into web and mobile apps.  Third, applying knowledge mining to uncover latent insights from documents.  

Today, at Microsoft Ignite, we are excited to announce a range of innovations across these areas to make Azure the best place for AI. Let me walk you through them.

Machine Learning 

From pre-trained models to powerful services to help you build your own models, Azure provides the most comprehensive machine learning platform.

To simplify development of speech, vision, and language machine learning solutions, we provide a powerful set of pre-trained models as part of Azure Cognitive Services.  When it comes to building your own deep learning models, in addition to supporting popular frameworks such as PyTorch

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24

Sep

Announcing automated ML capability in Azure Machine Learning

This post is co-authored by Sharon Gillett, Technical Advisor and Principal Program Manager, Research.

Intelligent experiences powered by machine learning can seem like magic to users. Developing them, however, can be anything but. Consider this “simple” tutorial chart from the scikit-learn machine learning library:

Source: scikit-learn machine learning library

Data scientists and developers face a series of sequential and interconnected decisions along the way to achieving “magic” machine learning solutions. For example, should they transform the input data, and if so, how – by removing nulls, rescaling, or something else entirely? What machine learning algorithm would be best – a support vector machine (SVM), logistic regression, or a tree-based classifier? What parameter values should they use for the chosen classifier – including decisions such as what the max depth and min split count should be for a tree-based classifier? And many more.

Ultimately, all these decisions will determine the accuracy of the machine learning pipeline – the combination of data pre-processing steps, learning algorithms, and hyperparameter settings that go into each machine learning solution.

Unfortunately, the problem of finding the best machine learning pipeline for a given dataset scales faster than the time available for data science projects.

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24

Sep

Global scale AI with Azure Cognitive Services

To build an effective and scalable solution, developers need technology that can be deployed around the world and still provide results with high confidence. To that end, we’ve spent the last year investing in making our Cognitive Services enterprise-ready and bringing them to general availability, ready for production use. Cognitive Services are a set of intelligent APIs and services that are used by more than 1.2 million developers and thousands of businesses throughout 150 countries across every industry from retail to healthcare to public sector to manufacturing and non-profit organizations.

We’ve deployed more services into the Azure data centers around the world, written more documentation in multiple developer languages, re-architected products to change the way we store and retain data in order to give controls to users over their data, adhering to the highest standards available. We’ve localized our services into multiple languages across the globe with over 10 of them now available in 15 languages. All while meeting strict SLA standards that we require for every Azure service. And we’re not stopping there, our work continues.

Just recently we’ve refactored our speech services and are launching a single unified speech service accessible via one endpoint to enable high speed

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24

Sep

Azure Databricks – Delta in preview, 9 regions added, and other exciting announcements

Azure Databricks provides a fast, easy, and collaborative Apache Spark-based analytics platform to accelerate and simplify the process of building big data and AI solutions that drive the business forward, all backed by industry leading SLAs.

Since announcing general availability in March, we have been continuously listening to customers and adding functionality to the Azure Databricks service. Today, I am excited to announce several new updates to Azure Databricks.

General availability Azure Databricks is now available in Japan, Canada, India, and Australia Central

We are excited to announce the general availability of Azure Databricks in additional regions – Japan, Canada, India, Australia Central, and Australia Central 2. These additional locations bring the product worldwide availability count to 24 regions backed by a 99.95 percent SLA.

We want to ensure that we build our cloud infrastructure to serve the needs of customers by driving innovation and making it accessible globally. Stay updated with the region availability for Azure Databricks.

Organizations also benefit from Azure Databricks’ native integration with other services like Azure Blob Storage, Azure Data Factory, Azure SQL Data Warehouse, and Azure Cosmos DB. This enables new analytics solutions that support modern data warehousing, advanced analytics, and real-time analytics scenarios.

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24

Sep

Ignite 2018 – Making AI real for your business with Azure Data

Today at Microsoft Ignite in Orlando, I shared how the confluence of Cloud, Data, and Artificial Intelligence (AI) is driving unprecedented change and is rapidly becoming foundational to innovation in every industry. The driving force behind powerful AI applications is data – and getting the most out of AI requires a modern data estate. Unlocking the potential of data has become an imperative for organizations that want to leverage AI to uncover new insights and create new business opportunities.

Today, we shared a number of exciting announcements that will enable organizations to build data and AI solutions that transform their businesses. For over two decades, organizations have relied on SQL Server to manage all facets of their relational data. Last year at Ignite, we announced the general availability of SQL Server 2017, which brought the much beloved SQL Server engine to Linux. With over 5.6 million downloads in just the first 6 months, it rapidly became our most popular SQL Server version yet. Today, we are building on that momentum with the announcement of the SQL Server 2019 preview. With SQL Server 2019, organizations can now seamlessly manage their relational and non-relational data in a single, integrated solution. It comes

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24

Sep

Build conversational experiences with Microsoft Bot Framework

Conversational AI is the next user interface paradigm in computing, making human and computer interactions more natural. We’ve evolved from a world where humans have to learn and adapt to computers to one where computers are learning how to understand and interact with humans. Conversational AI allows people to interact with computers in a natural manner including speech, graphics (cards), or text, and enables computers to learn and adapt to better understand us. Microsoft offers both Azure Bot Service and Microsoft Bot Framework to help develop such conversational experiences into your applications.

Azure Bot Service, Generally Available as of Dec 2017, provides a robust solution for connecting your Conversational AI to audiences on public channels like Microsoft Teams, Skype, Cortana, Facebook, and the web along with custom experiences in apps and on devices. The Azure Bot Service is SOC 1, SOC 2 and SOC 3 compliant, adding to the existing ISO 27001, 27018, PCI (DSS) and HIPAA/HITRUST compliance.

Today, I’m announcing the general availability of Microsoft Bot Framework SDK V4 for C# and JavaScript, and a set of cross-platform command line tools for managing bots, bots’ services, and channels. The Bot Framework Emulator V4 has a new version but remains

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23

Sep

Build conversational experiences with Microsoft Bot Framework

Conversational AI is the next user interface paradigm in computing, making human and computer interactions more natural. We’ve evolved from a world where humans have to learn and adapt to computers to one where computers are learning how to understand and interact with humans. Conversational AI allows people to interact with computers in a natural manner including speech, graphics (cards), or text, and enables computers to learn and adapt to better understand us. Microsoft offers both Azure Bot Service and Microsoft Bot Framework to help develop such conversational experiences into your applications.

Azure Bot Service, Generally Available as of Dec 2017, provides a robust solution for connecting your Conversational AI to audiences on public channels like Microsoft Teams, Skype, Cortana, Facebook, and the web along with custom experiences in apps and on devices. The Azure Bot Service is SOC 1, SOC 2 and SOC 3 compliant, adding to the existing ISO 27001, 27018, PCI (DSS) and HIPAA/HITRUST compliance.

Today, I’m announcing the general availability of Microsoft Bot Framework SDK V4 for C# and JavaScript, and a set of cross-platform command line tools for managing bots, bots’ services, and channels. The Bot Framework Emulator V4 has a new version but remains

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