Setting the file. One moment.
Skill 29 · Knowledge Base For Startups
Subchapter 29.181
references/learn/general/how-insightfinder-uses-aws-solutions-to-build-an-ai-driven-predictive-observability-platform.mdMarkdown7 KBView on GitHub
InsightFinder helps customers prevent outages in the cloud. See how they use unsupervised machine learning to make cloud infrastructure more reliable.
Holding onto your time—to be a spouse, to be a community member, to be an individual—is difficult, particularly in the world of startups. InsightFinder (opens in a new tab), an artificial intelligence (AI) startup that uses machine learning (ML) to help customers prevent outages in their cloud infrastructure, is on a mission to change that.
Founded by Helen Gu in 2016, InsightFinder uses unsupervised machine learning to make cloud infrastructure more reliable. The company’s AI-driven predictive observability platform helps companies to predict business-impacting incidents as well as pinpoint the root cause of impending incidents to avoid business loss and brand damage.
Helen says, “IT outages have a huge impact on everybody’s life. InsightFinder’s mission is to help everybody have a more reliable IT system.”
Fewer outages allow people more time to focus on doing what’s most important in their lives and for their businesses.
Alongside being the founder and chief executive officer (CEO) of InsightFinder, Helen is a professor at North Carolina State University and a distributed systems cloud computing expert whose work spans 20 years. “InsightFinder is created out of over 15 years of research work sponsored by National Science Foundation and industry partners,” she explains. “From day one, I’ve been very passionate about this field because I think it will affect a lot of people.”
Amazon Web Services (AWS) played an integral role in InsightFinder’s development on the cloud. “When we first started, in the early days, we were looking for something that was easy to use,” Helen says.
“AWS has a very nice program, AWS Activate (opens in a new tab), that gives a lot of credits to startup companies. We got quite a lot of credits that helped us to bootstrap our development. That played a critical role for us.”
It’s also thanks in part to AWS that InsightFinder has been able to build the high-performance Unified Intelligence Engine that fuels its success. The company leverages AWS solutions according to their needs, whether for CPU-intensive processes or I/O-intensive processes. “A lot of AI tech companies think you need to invest heavily in hardware resources,” says Helen. Through AWS, “We can actually build a high-performance engine, and with reasonable cost.”
By 2020, InsightFinder saw the problems that their customers faced were evolving, mostly due to the Covid-19 pandemic. One startup, Apprendis, faced a problem of scale as the number of students and teachers using their platform for science education, InqITS (opens in a new tab), grew quickly. The company is an active Amazon CloudWatch (opens in a new tab) user, but lacked the internal infrastructure to filter the alerts sent their way. By connecting the InsightFinder engine with the CloudWatch data, the company could receive essential insights quickly and easily. “It’s very simple,” says Helen. “A few clicks, and they can actually start to use the data and the predictions, and get the root cause analysis from AWS CloudWatch data through the InsightFinder engine.” Using CloudWatch data, InsightFinder caught hard-to-find software bugs and performance issues before end users noticed, which allowed the Apprendis team to scale their system without hiring DevOps engineers and to ensure seamless usage of its platform.
Now, InsightFinder is looking to the AWS Partner Network (opens in a new tab) to push the company to the next level.
“Being a fast-growing tech startup company, we don’t want to hire a large sales force to directly sell to a lot of customers,” Helen says. “AWS Partner Network is going to be an important go to market motion for us. It’s a more productive, effective, mutual, beneficial path for us.”
As fears that AI could impact job opportunities grow, Helen is clear that she doesn’t believe the goal of InsightFinder, or AI as a whole, is to replace people: “There’s no way we can have enough people, particularly skilled people, sitting there looking at all those charts for each machine and predicting future incidents.” Instead, Helen sees AI as a tool to augment human skill; that the technology should focus on things that humans cannot do. The algorithms that Helen works with fill the gaps that humans cannot, performing 24/7 work that is suitable for a machine, not a human being.
“The most precious thing in the world is time,” says Gu. “Our impact is that we want to give time back to people to do things they enjoy, rather than fix IT outages in the middle of the night.”
Curious about how AWS can help kick start your startup? Join AWS Activate (opens in a new tab) to build and scale your startup with the right resources at the right time.
Check out more AIML startups building and scaling on AWS 🚀:
Megan Crowley is a Senior Technical Writer on the Startup Content Team at AWS. With an earlier career as a high school English teacher, she is driven by a relentless enthusiasm for contributing to content that is equal parts educational and inspirational. Sharing startups’ stories with the world is the most rewarding part of her role at AWS. In her spare time, Megan can be found woodworking, in the garden, and at antique markets.