Time series modeling and forecasting is fundamentally important to various practical domains; in the past few decades, machine learning model-based forecasting has become very popular in both private and public decision-making processes.
Azure Machine Learning is a cloud service that you can use to track your models as you build, train, deploy, and manage them, all at the broad scale that the cloud provides. Francesca Lazzeri walks you through using Azure Machine Learning to build and deploy your time series forecasting models.
Outline
Introduction to Azure Machine Learning
Introduction to time series forecasting
Energy demand forecasting: A use case
Automated machine learning in Azure Machine Learning
Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the technology and operations management unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.
Aashish Bhateja is a senior program manager working on Microsoft Azure Machine Learning—building an exciting machine learning service that makes it easy for all data scientists and ML engineers to create and deploy robust, scalable, and highly available machine learning web services in the cloud.
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