Anodot is the only monitoring solution built from the ground up to find and fix key business incidents, as they’re happening. As opposed to most monitoring solutions, which focus on machine and system data to track performance, Anodot also monitors the more volatile and less predictablebusiness metrics
It feels like just yesterday we painted the walls of our office and started our journey of building Anodot, a machine learning SaaS platform for detecting business incidents in real time at massive scale. Since that day, two years ago, we’ve learned many lessons as we’ve grown our company.
The machine learning process can be summarized in five major steps as illustrated below:
We recently took a closer look at data made publicly available by the Bay Area Bike Share to see if we could find some anomalies by streaming the available data into Anodot's business incident detection system. The company, based in the San Francisco, set up bike rental stations throughout the Bay Area. Users can sign up for a 24-hour, 3-day or annual membership, which grants access to the bikes.
Like most web based businesses and SaaS companies, one of our go-to tools for a quick check of the usage of our service is Google Analytics. Typically we log in and we see the pleasing wave of application traffic, with the predictable seasonal drops during the weekend, and increases during the week. We get a daily report of usage per customer and look at the main flows in our service.
At Anodot, our solution analyzes the massive amount of metrics collected by data-centric businesses. These metrics originate from multiple sources, such as business processes, applications, systems, networks, and anything in between.
Like most of us, Eli Mordechai hates wasting electricity. However as Chief Architect & Technology Evangelist Service/Network/Environment Virtualization at HP, he has the skills to put his distaste for waste into action. To ensure that an air conditioner would never be left running in an empty room again, he hacked together several off-the-shelf tools to build his own smart home IoT system. He...
Anodot is the only monitoring solution built from the ground up to find and fix key business incidents, as they’re happening. As opposed to most monitoring solutions, which focus on machine and system data to track performance, Anodot also monitors the more volatile and less predictablebusiness metrics
It feels like just yesterday we painted the walls of our office and started our journey of building Anodot, a machine learning SaaS platform for detecting business incidents in real time at massive scale. Since that day, two years ago, we’ve learned many lessons as we’ve grown our company.
The machine learning process can be summarized in five major steps as illustrated below:
We recently took a closer look at data made publicly available by the Bay Area Bike Share to see if we could find some anomalies by streaming the available data into Anodot's business incident detection system. The company, based in the San Francisco, set up bike rental stations throughout the Bay Area. Users can sign up for a 24-hour, 3-day or annual membership, which grants access to the bikes.
Like most web based businesses and SaaS companies, one of our go-to tools for a quick check of the usage of our service is Google Analytics. Typically we log in and we see the pleasing wave of application traffic, with the predictable seasonal drops during the weekend, and increases during the week. We get a daily report of usage per customer and look at the main flows in our service.
At Anodot, our solution analyzes the massive amount of metrics collected by data-centric businesses. These metrics originate from multiple sources, such as business processes, applications, systems, networks, and anything in between.
Like most of us, Eli Mordechai hates wasting electricity. However as Chief Architect & Technology Evangelist Service/Network/Environment Virtualization at HP, he has the skills to put his distaste for waste into action. To ensure that an air conditioner would never be left running in an empty room again, he hacked together several off-the-shelf tools to build his own smart home IoT system. He...
