Data-sharing is an immense enabler of scientific advancements. When researchers choose to make their data publicly available, they are allowing their work to contribute far beyond the original findings. Making research data more accessible also fosters transparency and trust in their work, enables other researchers to reproduce and validate their findings, and ultimately, contributes to the pace of scientific discovery by allowing others to reuse and build on top of the data. Fortunately, cloud technology has made it easier than ever to share data across major research institutions. Empowered by tools that include Microsoft Azure, researchers are relying more and more on digital collaboration and open source software to drive scientific breakthroughs.
Watch this video to learn how researchers at Australian National University (ANU) are using Azure to build on each other’s findings and avoid a siloed approach to data that leads to slower research pipelines.
From autonomous vehicles, predictive analytics applications, and facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI span dozens of industries. Regardless of the AI application, though, these use cases all have a common aspect. After implementing thousands of AI projects, experts have come to realize that despite all of the diversity in applications, AI use cases fall into one or more of seven common patterns. One of them—the pattern-matching pattern—has allowed machines to digest large amounts of data to identify patterns, anomalies, and outliers in the data, so organizations can unearth previously undiscovered insights in their datasets.
In this article, you’ll learn how pattern-matching is being put to use in today’s organizations to prevent fraud, find the best job candidates, manage inventories in times of crisis, and empower data scientists with new perspectives on how to improve critical processes.
Hand in hand with digital transformation, cloud technology has seen outstanding growth. Because of the rapid pace of adoption, it has overtaken traditional on-premises hardware. Given the cloud’s huge potential across industries, verticals such as manufacturing, railways, banking, retail, education, and healthcare are switching to cloud services to optimize their reach and performance and increase their flexibility. But moving to the cloud is a strategic move, and it requires organizations to make careful internal analysis in choosing the features that will allow them to get the most out of their cloud migration.
By reading this infographic, you’ll learn more about the most important benefits and features of Microsoft Azure. You’ll also discover how the Microsoft cloud is helping businesses enjoy unparalleled cost savings, easily plan migrations, avoid the complexity of multivendor support, and modernize existing cloud applications.
View: Move and modernize your Microsoft workloads with Azure
One of the main challenges that modern researchers encounter centers on the size of their datasets. As research projects become increasingly more digital, the vast volumes of data that research generates demands enormous raw computing power, for both storage and computational capacity, which most organizations simply didn’t have access to. Cloud technology and artificial intelligence (AI) address this need, allowing researchers to free themselves from the constraints of their physical hardware. Cloud-based approaches can also boost the speed and efficiency of publishing, increasing the impact of research publications.
Subscribe now to learn more about the data management and automation capabilities of the Microsoft cloud and how they’re enabling breakthrough research projects around the world.
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Thanks to cloud technology, researchers can take advantage of faster, more advanced and more scalable resources that enable them to perform tasks that they can’t manage using in-house resources. The cloud also enables scientists to quickly run tests across a variety of platforms and virtual machines with different specifications, which can aid rapid prototyping, interoperability testing, and performance testing. A cloud solution can also provide researchers with ways to augment their existing resources by handling peaks in demand and reducing competition for local resources, when multiple teams are working toward an upcoming deadline. By reducing dependence on on-site computing, research projects can increase efficiency and scalability, and deliver greater results faster.
Watch this video to learn how Australian National University (ANU) is using Microsoft Azure to help decode more than 7 billion base pairs of DNA in the human genome and take its research further than ever before.
Thanks to AI, business applications will soon be able to answer complex questions, help users navigate interfaces, and enable cloud vendors to reduce support from personnel to manage their loads. AI is also being used in various software applications to help decision-makers identify and automate repetitive tasks, improving employee productivity. Increased understanding of how to better implement AI in business applications likely will lead to the emergence of new features, such as natural-language processing (NLP), that can help managers interact in more-intuitive ways with AI-powered apps.
In this article, you’ll learn about the innovative features powered by AI and machine learning (ML) that you can expect to see in business applications in the next five years.
In recent years, the global business landscape has witnessed a significant increase in cloud adoption. This has created significant value over traditional datacenters via greater scalability, cost-efficiency, and other performance improvements. Yet cloud migration requires careful planning. For a migration to be successful, it’s important for the business to come up with a strategy that also covers the end cloud environment, training, and—most importantly—the readiness of workloads and applications.
In this eBook, you’ll discover the key preliminary steps to consider in evaluating a cloud migration, as well as the various approaches for rehosting, refactoring, rearchitecting, and rebuilding your workloads for the cloud. You’ll also learn about some useful tools that you can use to accelerate your migration project and get tips to help you ensure post-migration success by using Microsoft Azure.
View: Cloud migration essentials
For modern organizations to truly take advantage of digital transformation, they need to be able to obtain, process, and extract insights from as much data as possible. This often puts a strain on businesses’ IT departments as they struggle to update legacy software and equipment to better handle the massive amounts of information available. Fortunately, the cloud has allowed businesses to move away from outdated technology and opened new opportunities for innovation.
Subscribe now to learn how the cloud, AI, and the data management capabilities of Microsoft Azure are driving increased efficiency at today’s leading businesses and organizations.
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Data is vital to scientific research. Without the ability to quickly process and analyze large amounts of data, breakthroughs and publications take longer. This increases administrative tasks and can even lead to projects going over budget. To avoid these pitfalls, modern researchers have begun turning to artificial intelligence (AI) and cloud-based technology, which enable the researchers to avoid the time and expense of setting up and configuring servers, leaving them to focus on their scientific goals.
In this video, you’ll see how researchers at Australian National University (ANU) conduct cutting-edge genome research by using Microsoft Azure to commission a data science virtual machine that allows them to instantly process large amounts of data. You’ll also learn how, thanks to the data management capabilities of Azure, ANU is now able to use unprecedented volumes of data and new analytical frameworks to decrease admin time and maximize the university’s research capabilities.
As scientific research becomes more and more complex, scientists need new, scalable ways to store and analyze ever-larger troves of data. They also need unprecedented amounts of raw computing power to process the data they obtain and deliver timely results that enable projects to stay on track and within budget. To ensure that their research adheres to these restraints, modern-day scientists need to increase efficiency via automation and other approaches whenever they can, wherever they can. Artificial intelligence (AI) and cloud technology give scientists the tools they need to advance research faster than ever before, get results quicker, and achieve scientific breakthroughs like never before.
In this video, you’ll learn how researchers at Australian National University (ANU) are using Microsoft Azure to dramatically shorten the amount of time they spend on data analysis, enabling faster publication of their cutting-edge genome research.