Etisalat Digital invites you to attend an Internet of Things workshop in association with The Assembly and PTC. In this session, we will take the engine data obtained on the cloud and use ThingWorx Analytics Server to create an engine failure prediction model based on the same. We’ll use Analytics to visualise and refine our machine learning model iteratively based on statistical methods. Our system will then be able to predict outcomes based on new incoming data in real-time with our early warning capacity automatically improving as more data is made available. Prerequisites: Activate your free PTC Cloud trial developer account - https://developer.thingworx.com/en/resources/trials (No download required)

Predictive analytics is a data analytics category that helps connect data to meaningful action by drawing accurate predictions about current circumstances and potential events. Enable the company to use predictive modeling to leverage trends detected in historical data to detect possible threats and opportunities before they arise. Here are a few use-cases that demand predictive modeling. Predictive Analytics helps executives and management to scale back risks, optimize operations, and increase revenue. Here are a few examples.

A leading technology provider and data-driven consulting organisation, and the Schulich School of Business at York University have announced a partnership to create a predictive analytics model that identifies and forecasts supply and demand of necessary resources in a disaster-related emergency. The model evaluates existing wildfire data points and feeds into an ad hoc trading platform which key stakeholders can use to option the right amount of services and supplies in the most cost-effective manner. The project aims to bring together local governments, insurers and medical supplies providers to collaborate and plan proactively for optimal disaster management. Available in June 2020, the platform is the first in a series of analytics tools that the Schulich School of Business and Exigent will develop to deliver on their core focus: turning data into actionable business intelligence and community-centric analytics products. The collaboration is part of the Masters in Business Analytics Program (MBAN) at Schulich.

Cognitive learning in computing is now more commonly used than ever. Typically, cognitive learning or cognitive computing means processes and technology platforms that cover the scientific disciplines of artificial intelligence (AI) and signal processing. AI is the latest trending factor of business growth and production, overtaking traditional levers such as capital investment and labor. It also has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business. More and more fields are discovering uses for Artificial Intelligence (or AI) than were ever expected thanks to its ability to process data, find patterns, and learn and recognize behaviors at an incredible rate.

Every website needs to provide a great user experience for its visitors. This is a principle that has been true since the earliest days of the Internet. However, it is becoming even more important, especially as user expectations are increasing and Google has started heavily relying on engagement statistics from analytics data as part of its ranking algorithm. This is one big reason that multilingual responsive design is so important and more achievable than ever. This has created some significant challenges for businesses that operate in different regions. If they don't offer the best possible user experience for those customers, then their rankings can drop for keywords some of the languages is that provide a lot of traffic.

The pace of technology-driven change is accelerating for enterprises all around the world. While the idea of artificial intelligence (AI) has been around for nearly 70 years, it wasn't until 2017 that we found 72 percent of business leaders believed AI to be a competitive advantage in the future (if not already), according to a recent PwC AI survey. In response, it's critical for companies to iteratively shift paradigms from legacy approaches to better compete in the age of digital transformation. Evolving software algorithms, capable of performing tasks typically requiring human intelligence, are fueling a wave of advancements in visual perception, speech recognition, decision-making, language translation, robotics and autonomous vehicle capability. Though AI is the catchphrase for numerous subfields, machine learning and deep learning are garnering the most attention as they teach themselves to learn, reason, plan and ultimately become more intelligent when exposed to bigger, more refined data sets and a standard predictive analytics model.

The pace of technology-driven change is accelerating for enterprises all around the world. While the idea of artificial intelligence (AI) has been around for nearly 70 years, it wasn't until 2017 that we found 72 percent of business leaders believed AI to be a competitive advantage in the future (if not already), according to a recent PwC AI survey. In response, it's critical for companies to iteratively shift paradigms from legacy approaches to better compete in the age of digital transformation. Evolving software algorithms, capable of performing tasks typically requiring human intelligence, are fueling a wave of advancements in visual perception, speech recognition, decision-making, language translation, robotics and autonomous vehicle capability. Though AI is the catchphrase for numerous subfields, machine learning and deep learning are garnering the most attention as they teach themselves to learn, reason, plan and ultimately become more intelligent when exposed to bigger, more refined data sets and a standard predictive analytics model.

Microsoft Azure Machine Learning (MAML) is a service on Windows Azure which a developer can use to build a predictive analytics model using machine learning over data and then deploy that model as a cloud service. ML Studio provides functionality to support the end-to-end workflow for constructing a predictive model, from ready access to common data sources, data exploration, feature selection and creation, building training and testing sets, machine learning over data, and final model evaluation and experimentation. In this presentation, we present an overview of the basic data science workflow, with details on select machine learning algorithms, then take you on a guided tour of ML Studio. During the presentation we will build a predictive analytics model using real-world data, evaluate several different machine learning algorithms and modeling strategies, then deploy the finished model as a machine learning web service on Azure within minutes. This end-to-end description and demonstration is intended to provide sufficient information for you to begin exploring ML Studio on your own after the session.

This ebook introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.

NOTE: The Microsoft Press Guided Tours app has been discontinued and is no longer available in the Windows Store. If you have already installed the app, you can continue to use it for as long as you like. All the tours will remain available for download from within the app. We're happy to announce the release of our newest free ebook, Microsoft Azure Essentials: Azure Machine Learning (ISBN 9780735698178), by Jeff Barnes. This is the third ebook in Microsoft Press's free Microsoft Azure Essentials series. Below you'll find the ebook's Foreword, by Scott Guthrie, Executive Vice President of the Cloud and Enterprise group at Microsoft, as well as a few helpful sections from its Introduction.