Building forecasts are an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. Building machine learning models is time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering. These choices multiply with time-series data, with additional considerations of trends, seasonality, holidays, and effectively splitting training data. Forecasting with automated machine learning includes capabilities that improve the accuracy and performance of recommended models using techniques like rolling-origin cross-validation, configurable lags, deep learning models and much more. Join us to dive into automated machine learning and its extensive forecasting capabilities.

Instructor's Bio

Sabina Cartacio

Program Manager, Azure ML at Microsoft

Sabina leads the product strategy for automated machine learnings forecasting and interpretability efforts. She has engaged with various customers in domains ranging from energy and utility, to large scale retailers. Well-versed in the business space, she aims to make machine learning possible for all skill levels from developers to data scientists.

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    Simplify and accelerate the development of forecasting models with Azure Machine Learning

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