No more blind spots.
No more “dark data".
Deep 360 monitoring™ technology leverages AI to both learn the behavior of every single metric in HD quality and map the network of correlations between the metrics in the data. Deep 360 then mines the stream of incoming data to rapidly identify and score anomalies. Backed by four patents, Deep 360 ensures fast and accurate monitoring of the organization’s most revenue-critical metrics.
Get visibility into
100% of your data
Deep 360 seamlessly aggregates inputs from storage, databases, analytics, monitoring, APIs and SDKs, CRM and data streams into one centralized analytics platform to analyze 100% of data streams and metrics, regardless of the business’s original data architecture and silos. Starting with your machine, application, and business data at its most granular level, Anodot’s unique technology engineers an HD view of your data aggregation layers and top line KPIs.
of metric behavior
Deep 360 leverages advanced AI and ML to learn the unique behavior of every metric and its weekly, monthly and annual seasonality—in real time and at scale, using Anodot’s patented Vivaldi Method. Every metric that comes in goes through a classification phase, and is matched with the optimal model from a library of model types for different signal types. Modified Holt-Winters, ARIMA and other algorithms are used for the sequential adaptive learning that initializes a model of what is normal on the fly, and then computes the relation of each new data point going forward.
& events correlation
Correlation is crucial for understanding metrics in context. Anodot uses a patented combination of four derivatives of behavioral topology learning: abnormal correlation, naming correlation, graph correlation, and implicit analytics topology. Scale is achieved through algorithmic metric partitioning and grouping, which enables to maintain rapid run time at any scale, without increasing computational costs.
Alert scoring and false
At Anodot, false positive reduction is our main KPI. Every alert counts. Alerts are scored according to deviation, duration, frequency, and related conditions. Anodot’s patented anomaly scoring method runs probabilistic Bayesian models to evaluate the anomaly delta both relative to normal, and relative to each other. Statistical models—such as ratios between metrics and influencing metrics—group and correlate different metrics in order to analyze them according to the specific business context. Feedback from end users is collected for each alert instance to further improve the system’s ML brain.
HD BASELINE AT SCALE
Blog Post 15 min read
The Key Principles of a Successful Time Series Forecasting System for Business
This in-depth article covers the value in using machine learning to create highly accurate, real-time, scalable forecasts for your business demand and growth.
Machine Learning 4 min read
Introducing 'MLWatcher', Anodot's Open-Source Tool For Monitoring Machine Learning Models
From biased training sets to problems with input features to not understanding context (i.e., ‘dumb AI’), problems abound when it comes to correctly and accurately tracking and monitoring machine learning models.
Machine Learning 5 min read
AI/ML - Are We Using It in the Right Context?
Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words. The trouble is that if you don’t know what AI/ML are, or what the difference is between them, then you’re that much more likely to be sold a bill of goods when you’re shopping for a product based on those technologies.