Tag: Machine Learning

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?

SmartWinnr is a gamified sales productivity platform that helps large distributed teams to sell smarter, better, and faster. The platform helps to drive sales results through virtual sales contests, remote video coaching, and virtual sales training. 

The Teraki platform, built by AI startup Teraki, automatizes intelligent sensor processing for telematics, video, and 3D point cloud data. The platform is developed with a single ideological concept/goal: Deliver scalability to manage the increasing need to handle sensor data from vehicles and devices in high volumes. Here’s how the team is leveraging AWS IoT services to do it.

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.

Education startup Honorlock has innovated exam integrity by introducing a browser extension rather than a software download, launching exam content protection technology (Search & Destroy™), detecting secondary devices during exams (Multi-Device Detection™), and providing human voice detection. They have also deployed a hybrid approach to exam proctoring, combining both AI and ML), with live human proctors (Live Proctor Pop-In™). Here’s how they’re doing it.

Utkarsh Gupta, Lead Data Scientist at 1mg.com walks us through how the healthcare startup is building a patient-centric digital health repository. In part 2 of this series, he discusses how the infrastructure described above can be used for large scale machine learning applications and the ways to deploy them in production.

Utkarsh Gupta, Lead Data Scientist at 1mg.com walks us through how the healthcare startup is building a patient-centric digital health repository.

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.

The mass digitization of information has made finding the right thing online difficult to say the least. This is precisely the problem Yewno was founded to solve. Leveraging sophisticated AI, built with AWS, the startup analyzes millions of information sources in real-time. Rather than simply hunting for keywords, the startup’s algorithms read text, understand context and meaning, and explain why things are connected.