CSPs aiming to achieve zero-touch networks are looking for monitoring solutions that go beyond autonomous detection to autonomous remediation. Ira Cohen, Co-Founder and Chief Data Science at Anodot explains the principles behind autonomous remediation and details the needed building blocks and capabilities to achieve it.

Chief Data Scientist Ira Cohen has spent years tackling the ins and outs of forecasting business data. Now he's sharing how he and his team at Anodot are using machine learning to generate more accurate forecasts for time series data.

Anodot’s autonomous network monitoring platform provides the ability to monitor cross-layer network performance and service experience in one platform. We collect all data types, at any scale, and use AI/ML to correlate anomalies across the entire telco stack. Our platform is the "brain" on top of the OSS that detects service-impacting incidents in real time. We help customers like T-Mobile and Megafon protect their revenue and improve service experience - reducing the number of alerts by 90% and shortening Time-to-Resolve incidents by 30%.

Welcome to our first ever customer event- Anodot Connect! During the power hour, we discuss best practices, talk about new features, the product roadmap and everything in between. Tune in to hear how you, our power users, can better make use of the Anodot product.

When you detect that something is off in your business, how long does it take you to find the root cause? The longer it takes, the more it can cost you. Correlation analysis identifies relationships between KPIs, which business teams use to accelerate root cause analysis (RCA) and mean time to remediation (MTTR). Doing it manually however can be tedious and limit your visibility.

Anodot collects AWS real-time usage metrics and AWS CUR files to enable full visibility. Anodot automatically learns each service usage pattern, using patented anomaly detection technology and alert relevant teams to anomalous spikes or drops in real-time. Our patented anomaly detection technology learns the behavior and every service you use - EC2, S3, ELB and the rest, to automatically identify any deviation from the expected usage and cost pattens. Leave alert storms, false positives, and dashboards behind and leverage the power of proactive, autonomous monitoring.

Now there’s an easy way to measure the business impact of every incident. Anodot lets you set a monetary value for each measure you monitor. Once you set the Impact Value, future alerts will show you how much the anomaly has cost you thus far. 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 predictable business metrics that directly impact your company’s bottom line.

Time series anomaly detection is a tool that detects unusual behavior, whether it's hurtful or advantageous for the business. In either case, quick outlier detection and outlier analysis can enable you to adjust your course quickly, before you lose customers, revenue, or an opportunity. The first step is knowing what types of outliers you’re up against. Chief Data Scientist Ira Cohen, co-founder of Autonomous Business Monitoring platform Anodot, covers the three main categories of outliers and how you'll see them arise in a business context.

Business metrics are notoriously hard to monitor because of their unique context and volatile nature. Anodot’s Business Monitoring platform uses machine learning to constantly analyze and correlate every business parameter, providing real-time alerts and forecasts in their context. This is machine learning packaged in a turn-key solution – no data science experience needed.

Co-founder and Cheif Data Scientist Dr. Ira Cohen of Anodot will present the challenges in leveraging AI for Autonomous network monitoring. The presentation will review the following topics.