Tag: AIML

Learn how Crate, an innovative generative AI startup founded by CEO Anna Bofa, builds their app on AWS to offer users an online storage system inclusive of all forms of digital artifacts.

Learn which AWS services can help you to build a generative AI application. The approaches discussed here can help your startup get its product to market as quickly as possible, while maintaining cost efficiency and high performance.

This introduction to generative artificial intelligence (AI) for startups explains various approaches to build generative AI applications and reviews their key components.

When startups build generative artificial intelligence (AI) into their products, selecting a foundation model (FM) is one of the first and most critical steps. Everything from user experience and go-to-market, to hiring and profitability, can be affected by selecting the right model for your use case. Learn about the most impactful aspects to consider when selecting a foundation model to meet your startup’s needs.

AarogyaAI, a healthcare and life sciences startup, is building with artificial intelligence and machine learning (AI/ML) on AWS. AarogyaAI rapidly diagnoses drug resistance in patients caused by bacterial, fungal, and viral pathogens. This allows clinicians to make data-driven treatment decisions and prescribe drugs that effectively treat and increase health outcomes for patients.

Building a cloud-distributed and scalable artificial intelligence (AI) application is a cross-team effort that requires complicated management of resources and comes with numerous production concerns such as code changes, refactoring, setting up the infrastructure, and complex developer operations (DevOps). These can confuse the development process, slow down time-to-market, and keep developers from focusing on product innovation.

For data to be useful in a modern enterprise, it must be collected and centralized from various sources, processed across a growing ecosystem of tools, and fed to systems across an organization in a way that’s consumable across teams. This data orchestration —weaving business logic through the data stack for everything from dashboards to personalization algorithms — requires hundreds, if not thousands, of data pipelines.

When time and resources are often stretched as far as they can go, and various branches of the infrastructure need to communicate, AWS can help startup founders and developers bridge the gap through Data insights to uncover customer needs. When paired with automation and machine learning, these services can put startups on a growth superhighway.

While Machine Learning can get quite complex, you don’t need a team of expensive Data Scientists and ML Engineers to gain real value from it. Check out our upcoming Twitch series for hands-on training with our AWS ML experts, and work through a variety of typical startup use cases from generating personalized customer recommendations to improving marketing efficiency.

Founded in 2018, Navina is leveraging the full AWS toolkit to improve the human-to-human interactions at the heart of healthcare. “[The result is] a better physician experience,” says Anne Amario, Navina VP of Marketing, as well as “better diagnosis and care.” Learn how Navina is driving better patient outcomes and preserving physicians’ revenues.