Industry trends

Manage credit risk: Credit Analytics debuts in Azure Marketplace

jeudi 18 octobre 2018

One of the biggest challenges banks and financial service organizations face is the sheer volume involved with monitoring and managing thousands and thousands of loans. Events like weather, earthquakes, geo-economic swings, and political shifts make it difficult to analyze the impacts on capital reserves, service operations, and more.

Principal Program Manager, Banking and Capital Markets

Eight use cases for machine learning in insurance

mardi 2 octobre 2018

Insurance companies that sell life, health, and property and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency. For example, the Azure cloud is helping insurance brands save time and effort using machine vision to assess damage in accidents, identify anomalies in billing, and more.

Principal Solutions Architect

IoT solutions for manufacturing: build or buy?

mardi 2 octobre 2018

If you are a manufacturer who wants to take its first steps towards IoT, and you’re overwhelmed by the plethora of vendors and IoT platforms in the IoT space, you are not alone. IoT is still a new space, with many moving parts and products.

Principal Manufacturing Industry Lead, Azure Industry Experiences Team

Cooling down storage costs in the healthcare AI blueprint

mardi 2 octobre 2018

Artificial Intelligence (AI) and Machine Learning (ML) are transforming healthcare. From streamlining operations to aiding in clinical diagnosis. Healthcare organizations are often challenged to begin an AI/ML journey due to lack of experience or high cost.

Principal Systems Architect, Microsoft Azure

Getting AI/ML and DevOps working better together

jeudi 20 septembre 2018

Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms.

Software Architect, Microsoft Azure