Here’s an overview of the conditions of AI adoption in Federal agencies, a look into the policy drivers likely to change the AI development equation, and guidance from experts at ViON and Dell Technologies Federal, who help agencies work their way through the process of mining greater data insight.
Traditionally, Federal IT applications were managed through a set of disparate point tools, each requiring human involvement. As applications grew out of the traditional data center into private cloud – and subsequently into public and hybrid clouds – the old way of doing things could no longer scale to meet the needs of Federal agencies.
Government mandates are pushing agencies to accelerate cloud migration, causing them to improve applications to make them cloud ready, while also consolidating data centers and connecting remote workers to new cloud environments. This is leading to a massive increase in application data, which needs continuous monitoring and analysis.
Artificial intelligence (AI) and machine learning (ML) are driving breakthroughs in innovation, but Federal agencies struggle to keep up with the growth of data that these technologies produce. IT leaders’ frustration with their agency’s data management strategies is driving new demands for secure and efficient data storage and management.
Across every level of government and industry, employees expect constant and reliable access to the data, applications, and tools they use to complete daily tasks. The onset of the global pandemic only accelerated this trend. Government agencies and contractors have been working to arm their employees with the tools and resources needed to remain efficient. To solve remote work challenges and maintain office productivity, agencies can implement a GPU-accelerated Virtual Desktop Infrastructure (VDI).
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Manufacturing for government projects requires stringent security to protect intellectual property. The onset of the global pandemic and remote work at scale forced organizations to move from physical workstations to a virtual workstation model to maintain productivity and security.
Today, the world is in the grip of a “digital revolution,” encompassing an explosion of emerging technologies, such as artificial intelligence (AI), robotics, nanotechnology, and more. This provides new opportunities and challenges for safeguarding data in a modernized environment.
Eighty percent of federal data today has a Geospatial Information Systems component. As the amount of data and project sizes continue to snow ball, federal teams need high-compute power at any location to process data quickly, effectively, and access that data.
Federal agencies have new opportunities to integrate emerging technology directly at the network’s edge, often in rugged environments. Accelerated processing at the edge is critical for AI-enabled applications, including disaster relief, intelligence gathering, or cyber security, where real-time insights are critical. More on building the digital future with solutions designed for unique mission requirements.
Deliver critical data and AI services without the associated IT resources and expenses. IBM Cloud Pak for Data as a Service is an integrated data and AI platform, fully managed on the IBM Cloud.
ViON on Demand is a service that allows IT organizations to dynamically order and use IT infrastructure – server, storage compute and data center networking – as needed, scaling its usage up and down to align with the organization’s unique requirements. Not only can the customer have this IT infrastructure installed on-premise, ViON on Demand allows for a high level of customization to suit the specific environment and need.
Data has become a critical advantage for information-driven agencies. By providing unprecedented access to actionable information, agencies can use data to better understand their operations, improve their services, and ultimately fulfill their mission requirements. To access this information, agencies need to be able to effectively operationalize data across their operations. This includes discovering and embedding past-, present-, and future-looking analytics into their end users’ workflow in order to move the metrics that matter.
Insider threats are not only limited to cyber attacks; malicious actions, or human errors within an organization can happen within any given industry. Organizations need agility to deal with new and unanticipated data and activities to stay ahead and respond to suspicious behavior across all their systems – during a time when best practices for insider threat mitigation are still emerging. Too often organizations notice wrongdoings long after the damage is already done.