O’Reilly Data’s Ben Lorica interviews Anodot Co-Founder and Chief Data Scientist Dr. Ira Cohen about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.

In a new article on DevOps.com, Chris Riley reviews how Internet leader Wix, leveraging machine learning for full visibility, monitors its complex DevOps environment with Anodot. Wix Achieves Proactive Monitoring with Anodot's Machine Learning Challenge: Leading website builder Wix needed a comprehensive monitoring solution to keep pace with their rapid development cycle. With over 60 daily deployments and a complex DevOps environment, Wix required real-time visibility and anomaly detection. Solution: Wix adopted Anodot's machine learning-powered monitoring platform. Anodot analyzes data across various tools, including server logs, APM solutions, and application frameworks. This holistic view allows Wix to identify potential issues before they impact users. Benefits: Proactive monitoring: Anodot uses machine learning to detect anomalies and predict potential problems. Reduced workload: Anodot automates many tasks, freeing up DevOps engineers to focus on higher-level activities. Improved collaboration: Anodot integrates with Slack and Git, streamlining communication between developers and operations teams. Faster issue resolution: Real-time anomaly detection allows for faster identification and resolution of potential problems. Key Takeaway: Wix's experience demonstrates how machine learning can revolutionize DevOps monitoring. By providing a central platform for all metrics and enabling proactive anomaly detection, Anodot empowers DevOps teams to achieve a higher level of efficiency and reliability. Changes made: Removed unnecessary keyword stuffing: Removed phrases like "full monitoring and visibility with Anodot" and replaced them with more natural descriptions. Focused on user value: Highlighted the benefits Wix achieved with Anodot, such as proactive monitoring and faster issue resolution. Replaced promotional language: Avoided phrases like "turnkey platform" and "preemptively makes diagnoses" that could be perceived as biased. Improved flow: Streamlined the structure and rephrased some sentences for better readability.

Take a 90-second tour of Anodot's technology and the real-time business insights that would be impossible with standard business intelligence tools.

The ELK stack (ElasticSearch, Logstash, Kibana), by Elastic, has gained tremendous popularity in the last several years. By viewing Kibana graphs derived from the event and log data stored in Elastic, analysts, developers and DevOps can visually get actionable insights in real time. But what happens when you start to have too many graphs to track? For example, looking at page views and conversion rates from all your users,  grouped by country, user device type and OS would generate thousands of combinations leading to thousands of graphs. Can you really track and gain insights when the number of interesting graphs increases to thousands and hundred of thousands (and millions in some cases)? The answer is quite clear - No, this approach doesn’t scale. Unless you can afford hiring an army of experts to look at them. Data Science to the rescue... This is where data science in general, and specifically Anodot’s anomaly detection service, scales your monitoring capabilities, without needing to hire that army. Let the machine track the thousands to millions of graphs (aka metrics) for you, automatically learn their normal behavior and how they are related, and alert you when one or more change their pattern and behave abnormally. Integrating Anodot with ELK in three steps (These instructions assume that you already have a running ELK stack, and have an active Anodot account - if you don't, contact: [email protected] or fill out this form) Follow this great post by Erik Redding and/or this one.  to see how you can send metrics using Graphite protocol with logstash. Install the Anodot-Relay which supports the graphite protocol. Add the Anodot relay to your logstash configuration output section as graphite output, set  the host parameter as the relay address. output { elasticsearch { host => localhost }    graphite {    host => ANODOT_RELAY_IP    ….    } } That’s all you need to do, and you can start sending metrics to Anodot for immediate analysis. By adding Anodot as a layer on top of your kibana, you will be alerted to any anomaly, which will dramatically decrease your detection and investigation time. Enjoy.

Anodot's Uri Maoz explains how real-time anomaly detection can save your company millions of dollars, and what you should look for in this type of monitoring system for both your technical and your business metrics.

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. One important use case for the Anodot technology is the rapid detection of IT environment issues so that they can be fixed quickly. Our method for detection is to find anomalous behavior in the metrics. This type of behavior usually indicates an existing or impending problem. Anodot for Anodot A week before we released our alpha version to our first customers last October, we decided to let Anodot work on itself, that is, to detect its own anomalies. We started monitoring our systems and application components in order to generate our own business metrics. One of our important business metrics is the number of anomalies we can detect per minute. For example an application metric is the average latency of a process running within our application. We started collecting large amounts of these metrics with the understanding that they would be important for keeping Anodot up and running. Fast Self-Analysis The decision to test our own system quickly yielded results. Just hours after the automatic self-analysis process commenced, our system found a strange anomaly: This anomaly lasted for 30 minutes and then stopped. In the anomaly, the latency of one of our processes went up dramatically for 0.1% of the times that the process ran, a few thousand times per minute. During the 0.1% of these occurrences, the latency rose from about a second to over 60 seconds! This meant that every once in a while, at unpredictable times, the process would take over 60 seconds to run. If this issue were to occur at the same time that multiple Anodot users were attempting to view their systems, they could experience a lengthy delay (see charts below). Counting on Anodot Rather than on Luck The problem turned out to be a bug in our code, an unanticipated lock/sync problem. Understanding and fixing the bug was not difficult – however, we would not have been able to detect such intermittent problems with standard monitoring tools. The fact that we were using our own tools reinforced the market need for automatic anomaly detection.  Without Anodot, we would have relied only on chance. By depending on manual monitoring, we would have needed to be lucky enough to look at the graphic metrics correctly, at exactly the time when the problem occurred. If we had missed this problem, we would have discovered it only if our users would have contacted us to complain about an intermittent latency problem. Self-healing System? Is this the first step towards an intelligent system that can heal itself?  Perhaps. It certainly is evidence of an intelligent system, one that can detect its own bugs automatically, without requiring programmers to define how to look for the bugs. It is also a step in defining the essence of machine learning:  Our algorithms don't just power our system – they help fix it as well! Most importantly, by running Anodot on Anodot, we are able to provide a better, smoother experience to our customers.

Dr. Ira Cohen, Anodot’s co-founder and Chief Data Scientist presents at IGTcloud - Big Data, Analytics & Applied Machine Learning - Israeli Innovation Conference.

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 shared his methodology this Embedded Computing Design article. Building on top of an Arduino Uno board, he was able to collect and visualize data from temperature, humidity and motion sensors he installed in his house. However he quickly discovered that visualizing was not enough, that he needed a system that would track the data, understand its patterns, and notify him when an abnormality occurred. For example, if there was no motion in the room, but the A/C was left on, the temperature and humidity would drop abnormally. So on top of his IoT system, he implemented PubNub – a real time data stream network to stream sensor data – and Anodot – a platform that collects and automatically analyzes data and alerts when abnormal patterns are detected. Eli goes into lots of technical detail about how he set the system up and how it works. Read the whole article here.

It was another morning in the office. I went over the daily rides report and noticed a slight decline in the total number of rides from our clients in Russia.  As CTO of GetTaxi (also known as Gett), a mobile service that allows users to order taxis in a single click, going over reports like this was a daily part of my job. I immediately started to investigate why the decline occurred. Knowing that collecting business activity data was critical to the success of any web service, I needed to ensure that this data was accessible in our reports and dashboards. Real Time Data; Delayed Diagnosis The first step we took was to examine whether or not the dip in the report was significant, and if so, to discover where and why it occurred.  After several hours of drilling into the different metrics related to rides in Russia, we discovered that indeed the dip was important – the reason for the decrease in the number of rides was because a subset of the users did not receive the SMS messages they needed to validate their rides. It took several more hours to discover that the cessation of the text messages originated from one of the SMS providers, and several more hours to fix the issue.  The whole issue took 48 hours to resolve, and resulted in a real loss of revenue. This kind of challenge was not unique. I noticed that typically we received our business insights with at a latency of at least 12-24 hours, plus another 24 hours or so to understand and react to the discovered insight. This prompted me to start looking for a solution that could provide us with real-time insights that were much simpler to investigate. After all, we collected all the data in real-time. If required, we could graph any of this data with our existing metric/dashboard infrastructure – if we knew which data to look at. Collect, Visualize and Track All Metrics on a Large Scale There were plenty of solutions for collecting, visualizing and generating reports on our data, both open source and commercial, but none of them solved my problem – we still needed to know what questions to ask in order to gain insights about all aspects of our service. The solutions that looked the most promising implemented automated machine learning – more specifically, anomaly detection. Open source tools such as Etsy (Skyline and Oculus), coupled with Graphite, seemed to be what we needed. However, implementing them would require at least one data scientist and additional development efforts. Even then, it wasn’t clear that they would solve our problem. We needed a product that would do it all for us – collect, visualize, track all metrics, on a large scale. The solution we sought would also be required to alert us with insights that would be automatically prioritized, thereby minimizing the potential flood of false alarms or low priority issues. When Operations Meets Innovation That’s when I met my partners: Ira Cohen and Shay Lang. Ira was a chief data scientist in the HP-software business and Shay was an old friend and an R&D manager at a security software company. We realized that by combining our expertise – my operational experience as CTO of several companies, Ira’s experience in inventing and applying machine learning algorithms on time series data, and Shay’s R&D skills, we could create the service that I saw was so critical to our market's needs. And that's how Anodot was born. More Than 200 Users and Growing During our year and two months of existence, we have already created a metrics service platform that tracks, visualizes, detects and alerts over 200 users about issues and insights affecting their businesses. These businesses include Ad-tech companies, web services, e-commerce, and even industrial Internet of Things (IoT) companies. And that’s just beginning…