iJungle is a tool to detect anomalies based on isolation forest, able to work and get value from each data point in very large datasets, with labeled or not-labeled data, and can be trained without the actual presence of anomalies. The algorithm is optimized to work with billions of data points in a reasonable time, is available to run on Azure Machine Learning and Azure Databricks and can offer explainability and fairness assessment. It has been tested in several industry scenarios, from anomaly detection in taxable transactions to discover fraud in the telecommunications industry.

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Instructor's Bio

Gustavo Pabon

Senior Delivery Data Scientist at Microsoft

Gustavo is currently a Senior Delivery Data Scientist at Microsoft organization called Customer Transformation and Innovation (Microsoft CT&I). He has been working with ML and AI since 2010 solving business problems for different industries having focus on CPG, manufacturing, and finance. He holds a M.Sc. in Computer Science with focus in Data Science from the Univesirty of Chile from 2015. 

Ricardo Castro-Garcia, PhD

Customer Innovation Data Scientist Manager at Microsoft

Ricardo has over 18 years of professional experience including technical, managerial, research and consulting positions in manufacturing, financial and tech companies. He holds an electronic engineering BSc and four post graduate degrees including a MSc and a PhD in Artificial Intelligence. Currently, Ricardo manages the Customer Innovation Data Scientists for Microsoft in Asia-Pacific-Japan and the Americas Time Zones. His team, spread over 7 countries and 3 continents, is at the forefront of the technology and is committed to bringing profound impact to Microsoft's biggest and most strategic accounts through the use of Artificial Intelligence and Machine Learning.


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    ON-DEMAND WEBINAR: iJungle: anomaly detection for really big data

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