By Ideas2IT Technologies
Client
Problem: A cloud security provider wanted to detect anomalies and abnormal peaks in outbound traffic using event logs. Solution: We set up a continuous feed of raw data logs, using AWS Kinesis from AWS Cloud-Trail. Data were then grouped by Time, User type, and Logotype into multiple batches. From these batches, variables were generated to feed into the Machine Learning model that finally predicted if the data entry was an anomaly using logistic [email protected] to bring your bold new ideas to life.
Problem: A cloud security provider wanted to detect anomalies and abnormal peaks in outbound traffic using event logs. Solution: We set up a continuous feed of raw data logs, using AWS Kinesis from AWS Cloud-Trail. Data were then grouped by Time, User type, and Logotype into multiple batches. From these batches, variables were generated to feed into the Machine Learning model that finally predicted if the data entry was an anomaly using logistic [email protected] to bring your bold new ideas to life.
Problem: An innovative finance company in the US wanted to launch a new Credit Card and Automotive Loans to low income individuals. They wanted origination and servicing applications. Rapid Go-To-Market (GTM) was of essence. Solution: Our in-house design studio worked with client’s marketing team to design an intuitive system for maximizing lead conversions. We used the latest tech stack (Angular 8, Ionic) to develop a Progressive Web Application that was responsive, personalized and supported English & Spanish. A highly scalable, cloud based, microservices solution was built on a DevOps environment that aided quick iterations to market. [email protected] to bring your bold new ideas to life.
Problem: It is hard to detect if a social profile is genuine or fake.Solution: The client provides a social identifier that is a free web identity credential controlled by the user. We designed and implemented an algorithm (based on research out of Stanford University) on Hadoop using PIG to evaluate the authenticity of an individual’s profile attributes using terabytes of social networking data. We also implemented various fraud detection and prevention solutions alongside viral social features to enable users to leverage their social graph to add to their assert [email protected] to bring your bold new ideas to life.
Problem: Our client had around 200 YouTube channels that covered 3 brands. They wanted to track over 2000 such brands for which only shallow data was available. Solution: We crawled millions of records to fetch data on audience analytics, along with cross-linked data from G+ & Facebook. We then used Coefficient Correlation and Linear Regression and provided the required analytics for 2000+ brands, using videos that covered just 3 [email protected] to bring your bold new ideas to life.