Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.

This post is the first in a two-part series, and is authored by Erika Menezes, Software Engineer at Microsoft. Visual content has always been a critical part of communication. Emojis are increasingly playing a crucial role in human dialogue conducted on leading social media and messaging platforms. Concise and fun to use, emojis can help improve communication between users and make dialogue systems more anthropomorphic and vivid. We also see an increasing investment in chatbots that allow users to complete task-oriented services such as purchasing auto insurance or movie tickets, or checking in for flights, etc., in a frictionless and personalized way from right within messaging apps.

Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. This curriculum is primarily oriented towards these two personas which meet the demands of Undergraduate and Postgraduate students. The target profile here is developer who is yet to use Microsoft AI tools and APIs to infuse intelligence into their applications. This profile relates to developers and data scientists who currently build AI and machine learning solutions and want to know how to do this with Microsoft's tools, framework and processes, such as the Azure Machine Learning Workbench and the Team Data Science Process. Services covered include, Cognitive Services and Azure Bot Services.

This post is authored by Chris Testa-O'Neill, Applied Data Scientist in the Microsoft Cloud AI team. Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to provide the best platform and tools to make them successful. You can read more Microsoft's approach to AI here. In keeping with our mission, we are currently running a series of popular AI boot camps around the world.

Azure Machine Learning services (preview) is an integrated, end-to-end data science and advanced analytics solution. It helps professional data scientists to prepare data, develop experiments, and deploy models at cloud scale. This Quickstart shows you how to create experimentation and model management accounts in Azure Machine Learning Preview. It also shows you how to install the Azure Machine Learning Workbench desktop application and CLI tools. Next, you take a quick tour of Azure Machine Learning Preview features by using the Iris flower dataset to build a model that predicts the type of iris based on some of its physical characteristics.

In the last two columns, I explored the features and services provided by Azure Machine Learning Studio. In September 2017, Microsoft announced a new suite of tools for doing machine learning (ML) on Azure. The cornerstone of these new tools is Azure Machine Learning Workbench. However, what could be better for doing ML than the simple drag-and-drop interface of Machine Learning Studio? Machine Learning Studio is an ideal tool for creating ML models without having to write code, but it falls short in several areas. First and foremost, the tool's simplicity requires a "black box" approach.

Azure Machine Learning services (preview) is an integrated, end-to-end data science, and advanced analytics solution for professional data scientists to prepare data, develop experiments and deploy models at cloud scale. This tutorial only prepares the data, it does not build the prediction model. You can use the prepared data to train your own prediction models. For example, you might create a model to predict bike demand during a 2-hour window. This tutorial uses the Boston Hubway dataset and Boston weather data from NOAA. The Hubway data is organized into files by year and month. For example, the file named 201501-hubway-tripdata.zip Create a new Azure Machine Learning project. Create a new data source. This displays the Data View. Select the icon and then select Add Data Source.

Fed up with the bribery, insider trading, embezzlement and money laundering committed by white-collar criminals? What if there was an app that could help nab these crooks by using the same machine learning tools and geospatial data increasingly relied upon by police to predict where the next burglary, drug deal or assault might go down? Sam Lavigne, co-creator of the White Collar Crime Risk Zones app, was onstage at the recent Strata Data Conference in New York, claiming to be able to do just that. "We used instances of financial malfeasance; density of nonprofit organizations, liquor stores, bars and clubs; and density of investment advisers," a straight-faced Lavigne said to an audience of data experts who immediately got the dark humor. For although the White Collar Crime Risk Zones app was indeed built -- using historical data from the Financial Industry Regulatory Authority -- its purpose is not to track white-collar crime, but to draw attention to the danger these kinds of applications, and the data they rely upon, present.

Microsoft has announced Azure Machine Learning Workbench application. Take a look at updates to machine learning and artificial intelligence with the Azure machine learning workbench. In this video, Microsoft Senior PM Manager for Microsoft Cloud, Sandhya Vankamamidi, will show you a new solution that assists in the construction and delivery of intelligent AI enabled data models. You'll see how it can be used efficiently to understand datasets through visualization. Increase the rate of experimentation, and monitor and manage your models wherever they're being used in production, all as part of a unified workflow.

Earlier today, we disclosed a set of major updates to Azure Machine Learning designed for data scientists to build, deploy, manage, and monitor models at any scale. This has been in private preview for the last 6 months, with over 100 companies, and we're incredibly excited to share these updates with you today. This post covers the learnings we've had with Azure Machine Learning so far, the trends we're seeing from our customers today, the key design points we've considered in building these new features, and dive into the new capabilities. We launched Azure Machine Learning Studio three years ago, designed to enable established data scientists and those new to the space to easily compose and deploy ML models. Before the term was in use, we enabled serverless training of experiments built by graphically composing from a rich set of modules, and then deploying these as a web service with the push of a button. The service serves billions of scoring requests on top of hundreds of thousands of models built by data scientists.