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Join us for IBM TechXchange Day: AI and Automation
In the era of AI ‘Time’ and 'Data' are the most important factors which ensure success of every business. Although it’s difficult to keep up with the pace of time, there are some powerful methods we can use to ‘see things’ ahead of time. One of the most popular use cases in machine learning aimed to enrich time based data is Time Series modelling. Time Series data very often requires a specialized type of storage, processing, modelling, and inference. Organizations utilize time series to solve critical problems such as detecting anomalies, optimizing staffing levels, managing inventory, forecasting future product demand, and many more. This session will introduce you to techniques and concepts in Time Series modelling based on various real-world scenarios.
Safura Suleymanova is a Data Scientist at the IBM Machine Learning Hub and develops Machine Learning powered solutions for customers across various industries, including finance, telecom, supply chain, among others. She feels passionate about how data can be used to streamline more routine processes ultimately improve business decisions. To improve existing operational processes at her previous workplaces she started leveraging the data, which led her to the field of Data Science. Safura has a bachelor’s degree in Mathematics, with a specialization in Risk Management and Statistics from University of Waterloo.