Machine learning (ML) is no longer just for tech giants; it has become mainstream, thanks to the cloud. The cloud eliminates barriers by integrating data, low-cost storage, security, and ML services with high-performance CPU and GPU computing. Its pay-as-you-go model allows businesses to control costs effectively.
As deep learning models become more complex, they require robust and scalable resources. The cloud provides powerful CPUs and GPUs, along with ample storage to support these advanced models.
Organizations can choose between fully managed services that handle infrastructure maintenance or self-managed solutions for more customization. This flexibility means you don’t have to invest upfront in extensive resources—everything you need is available on demand, keeping your ML tools and infrastructure up to date. Embrace the cloud to unlock the full potential of machine learning today!
Generative AI
What it is:
Generative AI is a type of AI that can create new content and ideas, including
conversations, stories, images, videos, and music. Like all AI, generative AI is
powered by ML models—very large models that are pretrained on vast amounts
of data and commonly referred to as foundation models (FMs).
How it’s used:
You can take advantage of ML for your business quickly and apply it to a broader
set of use cases with generative AI. Apply generative AI across all lines of
business, including engineering, marketing, customer service, finance, and sales.
Use generative AI to improve customer experience through capabilities such
as chatbots, virtual assistants, intelligent contact centers, and personalization.
You can also boost your employees’ productivity with generative AI-powered
conversational search, content creation, text summarization, and code
generation, among others.
Improve business operations with intelligent document processing, maintenance
assistants, quality control and visual inspection, and synthetic training data
generation. Finally, you can use generative AI to turbocharge production of all
types of creative content, from art and music to text, images, animations,
and video.
Use cases for generative AI
The use cases for and possibilities of generative AI span all industries and individuals. Here are the most popular applications to date, grouped by industry:
Life Sciences
- Creating novel protein sequences: Accelerate drug discovery and researchby creating sequences with specific properties for the design of antibodies,enzymes, vaccines, and gene therapy
- Designing synthetic gene sequences: Healthcare and life sciences companies can use AI-generated gene sequences for applications in synthetic biology and metabolic engineering, such as creating new biosynthetic pathways or optimizing gene expression for biomanufacturing purposes
Healthcare
- Creating synthetic patient and healthcare data: With simulated datasets, organizations can train AI models, simulate clinical trials, or study rare diseases—even when access to real-world data is unavailable or impractical—while complying with the strict security and privacy requirements of the industry
- Improving patient experience: Generative AI can personalize patient discharge instructions and treatment plans. Conversational assistants and chatbots can reduce clinician workload, increase patient satisfaction, and help provide proactive healthcare to at-risk communities
Financial Services
- Improving experiences: Financial services firms can better serve customers and employees by deploying chatbots that resolve problems faster, personalizing products and recommendations, and automating internal tasks—while still delivering the strong data encryption and privacy controls the industry requires
- Increasing knowledge-worker efficiency: Knowledge workers at financial firms can process applications faster, achieve deeper insights into customer behavior, improve collaboration, and deploy powerful training programs and simulations
- Analyzing market sentiment: Through faster and more thorough analysis of social media, news articles, and financial data, financial services firms can surface market commentary, identify opportunities sooner, and proactively mitigate risks
Media and Entertainment
- Speeding up content creation: From storyboarding and concepting to post-production workflows, media and entertainment companies can automate lower-level tasks to increase production speed and allow creative talent to iterate faster and realize the director’s vision
- Improving music: Artists can complement and enhance their albums with AI-generated music to create whole new genres
- Aiding the media supply chain: Generative AI applications can aid or automate tasks like localization, content moderation, and even content restoration
Education
- Summarizing text: Students and teachers can create concise summaries of research documents, lecture transcripts, and class notes to make them easier to search and browse
- Automating content creation: AI can transform information into sample test questions, accelerate grading, measure student performance across a wide range of factors, and provide personalized feedback and recommendations to teachers and students
- Personalizing learning environments: Educators can create personalized learning pathways for student segments—or even individual students—and leverage simulations and virtual reality to make learning more engaging
Automotive and Manufacturing
- Improving product design: Manufacturers can use AI to optimize the design of mechanical parts—or create entirely new material, chip, and part designs— improving quality and durability, lowering costs, and simplifying production
- Personalizing in-vehicle experiences: Virtual assistants and personalized route recommendations can enhance experiences for drivers and passengers
- Testing and maintaining: AI can improve product testing by generating information missing from datasheets—and unlock new assisted maintenance use cases to better maintain and service machinery, including products in use by consumers
- Improving overall equipment effectiveness for factories: Digitize and capture historical machine maintenance data, repair data, equipment manuals, production data, and potentially data from other manufacturers to generate suggestions for maintenance, repairs, or equipment parameters—and to improve productivity, availability, and quality
With the rapid growth and rising business value of generative AI, the number of use cases and applications for this transformative technology will only increase over time.
Autonomous systems lower costs and increase productivity
Autonomous systems use many different ML models to sense their environment and operate without human intervention. Autonomous systems rely on sensors, actuators, complex algorithms, ML systems, and powerful processors to execute software quickly.
Robots are one example of autonomous systems. As a purveyor of cutting-edge technologies, Amazon Robotics has long known that using AI and ML to automate key aspects of the fulfillment process represented extraordinary potential gains—so in 2017, it devoted teams to accomplishing just that. As the company iterated on its ML project, it turned to Amazon Web Services (AWS) and SageMaker, a managed service that helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly. This freed the Amazon Robotics team from the difficult task of standing up and managing a fleet of NVIDIA GPUs for running inferences at scale across multiple regions. As of January 2021, the solution saved the company nearly 50 percent on ML inferencing costs and unlocked a 20 percent improvement in productivity with comparable overall savings.
Put machine learning to work for your business
By running ML workloads in the cloud, enterprises get on-demand access to the most powerful GPU instances and ML tools that can be spun up in minutes, scale from one to thousands of instances, and keep infrastructure costs under control.
NVIDIA-powered AWS services and infrastructure are available for organizations of all experience
levels—from those that are seasoned in building ML workloads and want to manage their
infrastructure to those that prefer a fully managed approach. AWS supports your organization with
compute, networking, storage, and ML tools across each step of the ML development lifecycle,
including collecting and preparing data, choosing the right algorithm, tuning the model for
maximum accuracy, and deploying and monitoring model performance and quality over time.
