Category: Amazon SageMaker
AWS and Epiphany co-curate an AI/ML bootcamp called AI/ML Reactor. This rigorous 5-week virtual program aimed at driving AI/ML awareness and empowering startups in Pakistan and includes exclusive master classes, a group tech mentoring session, and one-on-one mentoring sessions with AWS specialists and thought leaders.
Carsome is Southeast Asia’s largest integrated car ecommerce platform. With operations across Malaysia, Indonesia, Thailand, and Singapore, they aim to digitize the region’s used car industry by reshaping and elevating the car buying and selling experience. Here’s how they’re using Amazon SageMaker to free up resources to innovate.
PulpoAR looks to bring together the digital and physical worlds using augmented reality. The company has launched its platform with the ability to virtually try on makeup online, but plans to expand into other categories, like skincare, in the near future.
Paladin AI is a company that uses machine learning to reinvent the pilot training process. Historically, aviation certifications relied heavily on subjective instructor scoring. The team at Paladin AI is looking to leverage data and ML algorithms to both make process easier and more accurate.
The body is often a mystery, and it’s nice when an external source is able to provide expert, evidence-based information about it. Flo App, a holistic health and wellbeing platform that helps women understand their bodies and minds, was built to do just that. Founded in 2015, Flo supports women as they make better informed decisions about their reproductive, physical, and mental health.
For most machine learning startups, the most valuable resource is time. They want to focus on developing the unique aspects of their business, not managing the dynamic compute infrastructure needed to run their applications. Productionizing machine leaning should be easier, and that’s where AWS comes in. In this blog post and corresponding GitHub repo, you will learn how to bring a pre-trained model to Amazon SageMaker to have production-ready model serving in under 15 minutes.
Overnight, the COVID-19 pandemic reshaped how and where Americans work. By June, according to a survey from Stanford researchers, 42% of the U.S. labor force was working from home full time, with millions more not working at all. For employers, that shift has led to new challenges as they navigate an unprecedented economy. One big question: what to do with all the empty offices?
The transition of the market research industry away from telephone and face-to-face interviews towards online platforms has massively increased the speed and reach of data collection. Modern online survey platforms, such as Dalia Research’s, allow millions of users every day to share their thoughts on politics, social issues, or consumer behavior. However, survey fraud is also on the rise. Here’s how Dalia’s leveraging machine learning to remedy it.
Finding the right data, both internally and externally, for your ML can be a huge pain, though. It’s often dirty, hidden behind paywalls, or just not enough to give a full view of a situation. This is where Explorium comes in.
Being able to choose really powerful instances to reduce your training time on demand, paying only for the seconds you use them, and at the same time having the choice of your notebook instances in your favorite tooling opens large opportunities for cost savings and productiveness across startups. AWS Startup Solutions Architect Manager Daniel Bernao walks us through how to do it.