Table of Contents
Introduction
Artificial intelligence (AI) has become crucial for many organizations as it offers numerous benefits in terms of efficiency and productivity. However, to leverage AI effectively, companies need to have the right infrastructure in place. This article will explore the infrastructure requirements for AI and how it can contribute to the growth of startups.
Leveraging Artificial Intelligence for Startup Growth
Infrastructure Requirements for AI
AI applications such as natural language processing, machine learning, and deep learning require an infrastructure that can handle high-performance compute and specialized needs. This infrastructure must be able to scale up and out easily to meet the increasing demands of AI workloads. According to a white paper, advanced AI applications necessitate a cutting-edge infrastructure that can provide the required performance, flexibility, and scalability.
Choosing the Right Cloud Provider
With the wide range of cloud offerings available, organizations need to carefully select the cloud provider that aligns with their strategic vision for AI. The capabilities of the cloud vendor and the ecosystem of partners and vendors around them play a crucial role in this decision-making process.
Case Study: Elekta
Elekta, a Swedish manufacturer of precision radiation therapy solutions, has embedded AI into its devices to increase access to personalized cancer treatment. By training their models in the cloud, Elekta can identify problems earlier and build resilience into their compute infrastructure.
Case Study: Wayve
Wayve, a London startup, is using advanced AI to accelerate and scale autonomous vehicle development. By leveraging Azure Machine Learning, Wayve can train its models 90 percent faster compared to its previous data center environment.
Case Study: Fashable
Fashable, a fashion startup based in Portugal, is utilizing AI and Azure AI infrastructure to create AI-generated clothing designs. This innovative approach allows designers to gauge interest and forecast demand before going into production, thereby avoiding the problem of overstock.
Case Study: Wildlife Protection Solutions (WPS)
WPS uses AI models powered by Azure’s purpose-built infrastructure to analyze remote camera images and aid in wildlife conservation efforts. By searching for suspicious activities and human-wildlife conflict, WPS can improve its conservation strategies.
The Growing Use of Purpose-Built Infrastructure for AI
As more companies adopt AI technologies like generative AI, access to purpose-built infrastructure will become crucial for deriving economic value from AI. Purpose-built infrastructure allows companies to quickly and economically leverage AI to transform their applications and drive business growth.
Conclusion
AI has the potential to drive significant growth for startups, but it requires the right infrastructure to be fully utilized. By selecting the right cloud provider and leveraging purpose-built infrastructure, startups can unlock the full potential of AI and gain a competitive edge in their respective industries.
Read the Harvard Business Review Analytic Services whitepaper
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