CloudFabrix
Service assurance is a key goal for digital-first businesses. It has become the lifeblood of their business applications. These applications are becoming more complex due to the advent of 5G, edge, and containerized cloud-native infrastructures. RDAF consolidates disparate data sources and converges on the root cause using dynamic AI/ML pipelines. Then, intelligent automation is used to remediate. Data-driven companies should evaluate, assess, and implement RDAF to speed innovation, reduce time to value, meet SLAs, and provide exceptional customer experiences.
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Subex Fraud Management
All types of frauds can be addressed with one solution. Subex Fraud Management is a 25-year-old domain expertise that provides 360 degree fraud protection across digital service by leveraging advanced machine intelligence and signaling intelligence. This solution combines a traditional rule engine with advanced AI/machine learning capabilities to increase coverage across all services and minimize fraud run time in the network. It also includes real-time blocking capabilities. The Subex Fraud Management solution's core is a hybrid rule engine. It covers detection techniques such as expressions, thresholds, and trends. Rule engine comprises of a combination of threshold rules, geographic rules, pattern (sequential) rules, combinatorial rules, ratio/proportion-based rules, negative rules, hotlist based rules, etc. These rules allow you to monitor advanced threats in your network.
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Dataiku DSS
Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.
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Edge Delta
Edge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need.
Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes:
* Shaping, enriching, and filtering data
* Creating log analytics
* Distilling metrics libraries into the most useful data
* Detecting anomalies and triggering alerts
We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost.
By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.
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