There are many exciting advancements made in the field of artificial intelligence (AI), like machine learning at the edge, explainable AI, and adversarial machine learning.
This rapid progression of AI is accelerating industry innovations, including medical imaging, speech recognition, robotics, logistics, and cybersecurity.
While many enterprises are exploring new use cases and possibilities for AI, a considerable number of IT teams, business units, and stakeholders still need to familiarize themselves with AI and analytics technology.
Businesses require a platform that gives them access to catalogs of AI tools and information to guide them along their AI journey and help them accelerate and implement AI technologies at scale.
Ronald van Loon is a NVIDIA partner and had the opportunity to discuss the new release of NVIDIA AI Enterprise 3.0 to support and accelerate business AI workloads.
NVIDIA AI Enterprise is a suite of software tools and technologies designed to help organizations deploy and manage artificial intelligence (AI) and machine learning (ML) projects at scale.
It includes a range of software libraries, frameworks, workflows, pretrained models, and tools for training, deploying, and managing AI and ML models in various environments, including on-premises data centers, cloud platforms, and edge devices.
The goal of the software suite is to provide a comprehensive set of tools and technologies that enable organizations and AI practitioners to more easily develop and deploy AI and ML solutions and to manage and maintain those solutions over time.
Enterprise AI Workload Challenges
Organizations can deploy popular advanced AI and analytics use cases like intelligent virtual assistants for contact centers, audio transcription, and cybersecurity digital fingerprinting to detect anomalies using cloud-native AI software.
AI software is designed to help organizations overcome their AI workflow challenges, running their AI workflows as microservices so they can develop applications and build AI solutions.
Here are the most common challenges businesses face when implementing and managing artificial intelligence (AI) and machine learning (ML) workflows at scale:
- Data Preparation: AI and ML models require large amounts of data training that requires fine-tuning, and this data must be properly collected, labeled, and organized to be used effectively. This can be time-consuming and resource-intensive, particularly for organizations with large or complex data sets.
- Model Development and Training: Developing and training AI and ML models can be complex and manually intensive. It is essential to have the necessary expertise and resources to do this effectively.
- Integration and Deployment: AI and ML models must be integrated with other systems and processes within an organization to be effective. This isn’t easy, particularly when dealing with legacy systems or complex environments.
- Model Maintenance and Monitoring: Once a model is deployed, it must be continuously monitored and maintained to ensure that it continues to perform well.
- Collaboration and Communication: AI and ML projects often involve teams of people with diverse skills and expertise working together towards a common goal. Ensuring that team members can effectively collaborate and communicate is a frequent obstacle, mainly when working with remote teams or members from different departments or locations.
Benefits of NVIDIA AI Enterprise Software Tools
IDC projects that by 2024, 60% of the G2000 will expand the use of AI and machine learning (ML) across all business-critical horizontal functions, such as marketing, legal, HR, procurement, and supply chain logistics.
A full stack software library with built in AI solution workflows, pre-trained models, and infrastructure optimization will aid global organizations in keeping their AI project goals on target.
There are several potential benefits of using AI enterprise software to help organizations deploy and manage artificial intelligence (AI) and machine learning (ML) projects at scale:
A validated platform for efficiency and productivity: By providing integrated tools and technologies certified to run anywhere across the cloud, data center, and edge, organizations can easily develop and deploy AI and ML solutions with improved efficiency and productivity.
Accelerated time to production: To reduce the complexity of developing common AI applications, NVIDIA AI Enterprise includes AI workflows that are easy-to-use reference applications for specific business outcomes such as Intelligent Virtual Assistants and Digital Fingerprinting for real-time cybersecurity threat detection. Developers can deliver production-ready applications with greater accuracy and performance even faster.
Scalability: Supports the deployment and management of AI and ML solutions at scale, making it well-suited for large organizations with complex data pipelines and diverse environments.
Expertise and support: Reliable support is vital to both IT teams who deploy and manage the lifecycle of AI applications and AI practitioners who develop mission-critical AI applications. Accessibility to expert support and resources, including training and professional services, can help organizations implement and manage their AI and ML projects more effectively.
Better accuracy and performance: A set of tools and technologies that enable organizations to develop and deploy high-quality AI and ML models more efficiently allows businesses to enhance the accuracy and performance of their AI and ML solutions.
Embedding AI into Financial Services
AI is essential in financial services to enhance customer experience and build stronger customer relationships in a competitive industry. In addition, AI is used to develop new financial products and services tailored to the needs of specific customer segments or that take advantage of new technologies and trends.
Traditional business models within the financial services industry are becoming disrupted due to AI by enabling new entrants to enter the market and changing the way existing firms operate.
Deutsche Bank is undergoing a significant cloud transformation and requires AI and ML to streamline cloud migration decision-making. Like many financial service organizations, Deutsche Bank is particularly challenged by unstructured data like customer emails, social media posts, and customer service transcripts, as most currently available large language models don’t perform well on financial text.
Unstructured data can come in many formats, making it difficult to standardize and organize. Unstructured data must often be integrated with structured data to be useful. Financial services organizations are subject to strict regulations and compliance requirements, and it is vital to ensure that unstructured data is efficiently managed to meet these requirements.
By combining Deutsche Bank’s financial experience with NVIDIA’s AI and accelerated computing, they can provide next-generation risk management, reimagine customer service with interactive 3D avatars, and extract key insights from their unstructured data.
Deutsche Bank is now well-positioned to explore the development of AI and ML services and expand AI skill development across the enterprise. They can also promote explainable and responsible AI in their financial model predictions and applications.
Enterprise AI Everywhere
Organizations across industries must accelerate their AI journey with software tools and capabilities that make implementing, deploying, and managing AI and ML user-friendly.
Implementing AI solutions and applications while supporting and optimizing AI workloads with NVIDIA AI Enterprise that helps organizations accelerate data preparation, training, and deployment at scale.
Businesses can learn to use and work with existing AI frameworks and pre-trained models and run AI solutions across multi-cloud, hybrid-cloud, and edge environments, flexibly deploying AI everywhere.
By Ronald van Loon
