Category: AWS for Startups

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.

AWS is launching AWS Startup Ramp in France to accelerate the development of early stage startups in the public sector. This tailored program supports startups in developing new products and services in government technology, healthcare, sustainability, smart cities, space technology, and more.

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.

Though their personal experiences in the lab were different, Sara and Gabriel, PhD student, came to the same conclusion: research labs are seriously lacking when it comes to accessibility. It’s a pervasive problem not limited to one institution or type of disability. That’s why they decided to collaborate with LabVoice—a digital lab assistant platform designed specifically for the research lab. Working together with the LabVoice team, they developed an inventory search solution that allows users to record information, like chemical location and amount, and then retrieve it later entirely through verbal prompts.

No matter what market you’re in, successful startups all have a few things in common—a passion and commitment for what they’re doing, a great story to tell and a laser-like focus on customer needs. Dataiku has taken the data and AI world by storm—transforming from French startup to global unicorn in just seven years. The team’s journey began with a passion for data and machine learning (ML) and a quest to bring everyday artificial intelligence (AI) to companies of all sizes and sectors.As a founder, your path will of course be different from Dataiku’s, but they can show you what to look out for and provide advice to aid and speed your progress.

What do you do if you don’t have the resources, time, or funds to self-manage a database? Use Amazon Relational Database Service (RDS)! Amazon RDS allows you to set up, operate, and scale a relational database in the cloud with just a few clicks. It removes inefficient and time-consuming database administrative tasks without needing to provision infrastructure or maintain software. And, with the new AWS Database Plug & Play Program, you’ll get a packaged bundle of AWS advisory time, architecture and prototyping patterns, AWS usage credits, and pathways to go-to-market acceleration programs to help further ensure your teams can focus on value-generating work.

Originally, Fyle’s Data-Extractor service relied on an external service provider for optical character recognition (OCR) and Fyle’s internal machine learning algorithm to detect amount, category, date, currency, and vendor information. Unfortunately, they were receiving some feedback from customers that their tool wasn’t very accurate. As you can imagine, this isn’t the best place to be, so they rewrote their Data-Extractor service to use Amazon Textract because of its intuitive web console for APIs, which allowed them to test APIs in real-time with personalized input. This let them quickly try out an Amazon Textract API, which helped them achieve their goal of turning around a solution in two months. After implementing their new solution, Fyle saw 51.7% improvement in accuracy for the Data-Extractor service.

Understanding your data types and their sensitivity levels ensures that your startup stays ahead of unintended data use or disclosures and satisfies compliance requirements. By identifying the data you have and implementing appropriate, automated controls, you can meet these requirements more easily, while also improving your security posture. To get you started, this post provides four simple steps to simplify and automate the data classification process for your startup.

By using Serverless on AWS to scale their infrastructure, the cinch team was able to focus their cognitive load on improving the platform, quickly release new features, and re-build existing ones based on real world customer insight. With their new architecture, cinch was able to pivot their business to the new model in 6 months, increase traffic by 2.5x (6,000 to 16,000 requests per minute), and reduce latency. They went from hundreds of cars sold within days, and grew by a factor of 100x within a few weeks.

It’s almost the most wonderful time of the year again: AWS re:Invent is just around the corner. And while we’d love to hang out with you in Las Vegas from November 28 through December 2, never fear if you can’t attend in person. You can still get everything re:Invent has to offer by attending virtually. (Well, except for the slot machines and general Vegas-fueled sensory overload, of course!)