Despite the immense potential of increasingly sophisticated artificial intelligence (AI) to boost performance, efficiency, growth, and customer experiences, not all organizations are ready to enjoy AI’s benefits.
In sectors such as healthcare, pharma, biotech, manufacturing, and finance, some organizations struggle to build the powerful, unified infrastructure they need to manage AI’s steep processing, data, and security demands of applying reliable large language models (LLMs). Most also lack the necessary expertise to build AI-driven strategy and keep pace with compliance changes.
But new cloud-based innovations such as AI infrastructure tools with accelerated computing capabilities and greater processing power are helping thousands of businesses of all sizes and budgets in numerous applications.
For healthcare, biotech, and pharma, these powerful AI-driven capabilities bring the potential for faster clinical research and drug discovery and for more efficient identification of optimal candidates for precision medicine. For manufacturers, it means the potential to develop industrial digital twin simulations. And for many sectors, it means faster discovery of commercial opportunities: designing new products and entering new markets.
Challenges to Building an AI Strategy
For all its promise, implementing a successful AI strategy is daunting. Many organizations are just getting started with exploring AI and determining use cases. But while three out of five business leaders believe the AI market is growing, virtually every organization struggles to scale and bring AI online, according to The State of AI Infrastructure, an annual report from Microsoft.
One key challenge to seeing returns on AI investment is the immediate need to source AI experience and talent, calling for organizations to rapidly boost employees’ AI skills and training.
Other roadblocks are technological. Organizations must build infrastructure robust enough to handle AI’s high-performance processing and data requirements for training resource-intensive LLMs. AI infrastructure is pivotal to ensure fast response times, user experience, cost optimization, and scalability for deploying LLMs in production environments.
Ensuring data security and privacy is another obstacle in highly regulated sectors such as life sciences and finance, which may introduce strict and complex compliance requirements for implementing AI solutions.
Taken together, these challenges make state-of-the-art tools for AI development and data as well as data management that work with scalable cloud-based compute infrastructure essential for developing these machine learning (ML) models and training, fine-tuning, and customizing their LLMs to optimize performance and deploy at scale.
Implementing AI successfully requires organizations to deploy a full-stack approach including the developer tools, applications, and services combined within a purpose-built AI cloud infrastructure that’s cost-efficient, scalable, and future-proof.
Accelerating Innovation in Medicine
Healthcare and life sciences are already seeing the benefits of advanced AI-driven computing on these robust platforms. With these two sectors, the acceleration of innovation and improving patient care can change—and even save—people’s lives.
Health care and biotech organizations can now access powerful generative AI for clinical research and drug discovery that simplifies and accelerates the training of its models on proprietary data for drug discovery. Using a Gen AI platform that can analyze vast proprietary datasets with unprecedented precision speed, and security, these organizations can now identify drug candidates faster than ever.
Deployed at scale, AI’s emerging capabilities of effective development, validation, deployment, and evaluation mean organizations’ developers can build more accurate medical imaging AI models, clinical researchers can accelerate drug discovery, and healthcare providers can integrate a vast range of third-party AI models into their clinical workflows on one robust cloud-based platform.
Building with Better Digital Twins
Manufacturers are also implementing powerful AI application programming interfaces (APIs) to experiment, build, and distribute products more quickly and economically with digital twins: virtual simulations and workflows that can precisely analyze vast data sets for predictive models.
Manufacturers can now tap a suite of these advanced APIs to give users such capabilities as generating precise data-based renders, enabling scene queries and interactive scenarios, and connecting users, tools, and worlds for more advanced collaboration than ever. By integrating these APIs into existing design and automation applications for digital twins or into their existing workflows, organizations’ developers can accelerate the development and manufacture of such categories as robotics and self-driving vehicles.
Large industrial-software corporations are introducing these AI-powered cloud-based APIs into their portfolios.
Making Sense of LLMs
One significant hurdle to introducing AI has been LLMs, which require enormous amounts of data and compute to train and run.
Using a single integrated AI platform with robust processing power can accelerate time-to-market, differentiate products and services, provide built-in security, and support even the most rigorous compliance requirements economically.
Powerful graphics processing units (GPUs) are crucial for building, training, and deploying LLMs. Traditional central processing unit (CPU)-based systems alone cannot meet the sheer computational requirements of building and deploying large transformer-based language models.
Organizations can unlock the full potential of LLMs and achieve greater performance and accuracy while minimizing total cost of ownership (TCO) by using the immense processing capabilities of state-of-the-art GPUs. Snorkel AI depends on purpose-built cloud-based AI infrastructure to power its most demanding ML workloads, simplify AI deployment, and streamline management.
Boosting Business with a Powerful AI Platform
The integration of cloud, AI, and supercomputing is helping transform compliance-heavy industry sectors such as finance and healthcare and biotech. The combination of global scale, security, and advanced computing cloud capabilities is enabling developers at healthcare organizations to develop AI capabilities that accelerate innovation and improve patient care.
Navigating the complexities of the dawning AI era starts with an investment in sophisticated, responsible, and secure AI technologies. A comprehensive cloud-based ecosystem that infuses AI across every layer of an organization’s technology stack can empower your organization’s employees to achieve productivity gains and operational efficiencies that can translate into better customer experiences.
Learn more about Microsoft and NVIDIA solutions for AI.
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Copyright for syndicated content belongs to the linked Source : Harvard Business – https://hbr.org/sponsored/2024/04/how-cloud-based-ai-infrastructure-is-shaping-tomorrows-businesses