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.
The super.AI platform helps customers to transform processes involving unstructured data such as images, videos, text, documents, and audio and automate them using a combination of AI, software, and humans. Their customers requested a more efficient, highly accurate labeling mechanism, so they eleased a new feature where the pipeline pre-processes data points using an ML model running on Amazon SageMaker.
When Gil Elbaz and Ofir Zuk founded Datagen in 2018, it was with the purpose of re-inventing the broken process of how clients obtain data for computer vision network training. More specifically, they wanted to bring data simulation to every computer vision team in a continuous and scalable way.
Nonprofits are often overwhelmed by the amount of data they accumulate, and lack the resources to generate value from it. In response to this challenge, charitable organization Data Science for Social Good (DSSG) Berlin was founded. As part of their mission to enlist volunteer data scientists and analysts to help nonprofits use their data properly, DSSG Berlin hosted Datathon, a data science hackathon powered by AWS services.