As hundreds of AI initiatives and programs are underway across the Department of Defense, many are facing new and diverse challenges when it comes to operationalization. Selecting a solution and putting it into practice are certainly not the same task, creating challenges that span both organizational and data facades. For example, the rise of hybrid and multi-cloud architectures present data integration, access and management challenges for many as agencies seek to leverage data assets that span across both on-premise and hosted solutions. This may be why the GSA Data Center and Cloud Optimization Initiative Program Management Office recently released a Multi-Cloud and Hybrid Cloud Guide to help agencies make better decisions about cloud architecture. Further complicating matters is that the DoD is facing a groundbreaking December award of the Joint Warfighting Cloud Capability procurement. Pentagon Chief Information Officer John Sherman describes the JWCC as a "multi-cloud effort that will provide enterprise cloud capabilities for the Defense Department at all three security classifications: unclassified, secret, and top secret all the way from the continental United States out to the tactical edge."
Outward Media, Inc. (OMI), a leading provider of multi-channel marketing data, announced it has partnered with Snowflake, the Data Cloud company, to launch its high-quality B2B contact data on Snowflake Marketplace, a centralized platform where customers can securely access live, ready-to-query data to unlock insights with just a few clicks. New sample data sets from OMI are now available on Snowflake Marketplace, along with data from many other third-party data providers and data service providers. With OMI data, Snowflake Marketplace customers can choose from a wide range of decision-maker titles--from manager-level to CEO--across industries. After testing the sample files, joint customers can work directly with OMI to license complete data sets, leveraging firmographics or digital intent signals to build custom audiences that meet their precise marketing needs. In addition, Snowflake customers can take advantage of OMI data on the Snowflake Media Data Cloud to dynamically share, join and analyze collaborative data for identity, audience insights, targeting, activation and measurement.
Note: This is the third of a five-part series covering Kubernetes resource management and optimization. In this article, we explain how machine learning can be used to manage Kubernetes resources efficiently. Previous articles explained Kubernetes resource types and requests and limits. As Kubernetes has become the de-facto standard for application container orchestration, it has also raised vital questions about optimization strategies and best practices. One of the reasons organizations adopt Kubernetes is to improve efficiency, even while scaling up and down to accommodate changing workloads.
From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity." However, being acutely aware of the concerns around intelligent technology, the company's AI research division Google Brain recently published an AI Precision Safety whitepaper. Powerful Infrastructure Underpinning all of these projects, as well as the company's flagship Search, Translate and Youtube products is Google Cloud Platform, providing developers with the tools to build a range of programs from simple websites to complex, intelligent applications. As part of our AI in Business Festival, we spoke to Miles Ward, Global Head of Solutions at Google Cloud Platform, to find out more about the machine learning tools they offer to developers. From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity."
Integrating AI effectively enables businesses to learn and act on information, making predictions, automating operations and optimizing logistics. AI might add $16T to the global economy by 2030, yet 81% of company leaders don't comprehend the data and infrastructure needed. Modernizing information architectures (IA) with AI requires a prescriptive, strategic approach. Successful AI models require data gathering and organization; uniform and open information architecture is needed to prepare enterprises for an AI and multi-cloud environment.
Pre-COVID, agility became an aspiration and rallying cry for organizations seeking to embrace emerging technologies and pursue technology-enabled innovation, often to stave off digital disruption in their industries. Once the pandemic hit, that nice-to-have became an existential necessity. "As CIO, I'm constantly looking at ways to become more agile and using IT as a strategic differentiator," says Scott duFour, global CIO at digital payment solutions company Fleetcor. "This goes beyond implementing agile methodology. It's the ongoing assessment of how we can run our current systems more efficiently to meet our digital transformation goals."
Just as Justin Silverman predicted in his 2022 Legal Trends and Predictions Outlook, AI and machine learning are not only buzzwords, but realities on the roadmaps of many competitors (if they have not already been implemented, that is!). As more teams turn to AI-driven analytics to ensure data accuracy and mitigate risk, organizations are realizing that they will need to leverage these technologies in order to stay competitive. In a recent HBR survey, two thirds of respondents said that machine learning would be the most important emerging technology to their organization, more than half selected "internet of things" and 41% said deep learning. These emerging technologies depend on cloud capabilities, and so companies must begin thinking not only about the technologies themselves, but about the cloud infrastructure that they are built upon. It's critical to look for a partner that can deliver: This way, companies can reap the benefits of cloud computing-- low installation costs, automatic updates, etc.-- while also entering into a larger partnership that can scale and evolve to meet the unique goals and objectives on their roadmaps.
Previously, it was the norm for several data-oriented tools, products, and offerings were to be used to address different requirements. Even in the IBM portfolio, enterprises turned to Planning Analytics, Cognos Analytics, and Watson for specific purposes. In some enterprises, this created a situation where there were individual instances of these powerful tools running in the tech ecosystem. While the tools offer great value in themselves, the sense was always "What if we could get all this goodness together?" That's when IBM created a new all-around offering in the form of Cloud Pak for Data.
Welcome to Azure Hybrid, Multicloud, and Edge Day--please join us for the digital event. Today, we're sharing how Azure Arc extends Azure platform capabilities to datacenters, edge, and multicloud environments through an impactful, 90-minute lineup of keynotes, breakouts, and technical sessions available live and on-demand. Now you can build, train, and deploy your machine learning models right where the data lives, such as your new or existing hardware and IoT devices. When I talk with customers, one of the things I hear most frequently is how new cloud-based applications drive business forward. And as these new applications are built, they need to take full advantage of the agility, efficiency, and speed of cloud innovation. However, not all applications and infrastructure they run on can physically reside in the cloud.