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How to Improve Data Labels and Feedback Loops Through High-Frequency Sensor Anomaly Detection by Using InfluxDB
Webinar Date: 2020-12-01 08:00:00 (Pacific Time)
Ezako is a startup specializing in time series analysis. Ezako helps its clients detect anomalies and label their time series data. It helps accelerate the labeling process and analyze vast amounts of data from a variety of sensors in real-time. The company provides anomaly insights and makes it easier for data scientists. Ezako is the creator of Upalgo, which is a time series data management tool that uses AI to automatically detect anomalies in streaming data.
During this webinar, Ezako will dive into how high-frequency sensors can generate huge amounts of data which can become desynchronized. This can result in data quality issues as it can contain errors and glitches. Ezako uses machine learning, labelling and feedback loops to identify these errors. Discover how the company helps improve its clients’ data quality and reduce the number of validation mistakes.
Julien is the technical lead at Ezako. Prior to joining Ezako, Julien worked for 12 years at IBM as a Big Data Architect and Analyst on heterogeneous data in California and in France.