AWS Startups Blog

A Startup’s Guide to AWS Services Series 1: Making Security the Cornerstone

The security of our customers is the top priority for AWS, and especially true for startups in their initial stages of operation.  As an AWS customer, you get the most flexible and secure cloud computing environment available today and benefit from the AWS data centers and network that are architected to protect your information, identities, applications, and devices.

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How Navina Leverages the Full AWS Toolkit to Make Data Work for Doctors and Patients

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.

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Automating Unstructured Data Processing with Amazon SageMaker

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.

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AWS Launches $30 Million Impact Accelerator for Underrepresented Founders

Learn more about the new program that will provide up to $225,000 in cash and credits for early stage startups led by Black, women, Latino, and LGBTQIA+ entrepreneurs, as well as training, mentoring, and technical guidance. Then hear from three founders about what access to capital and resources means for the next generation.

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Video: DayTwo Is Using Data Analytics from the Gut Microbiome to Further Healthcare

Data infrastructure relies on a variety of data analytics tools and machine-learning capabilities, so DayTwo turned to several of the AWS ecosystem services. Specifically, they are utilizing AWS Lake Formation and AWS Deep Learning Containers in order to analyze large outputs. They’re also relying on Amazon SageMaker to manage all of their machine-learning and AI capabilities.

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