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Srini Penchikala: My name is Srini Penchikala. I am the lead editor for AIML and data engineering community at InfoQ website. Thank you for checking out this podcast. In today’s podcast, I will be speaking with Dr. Francesca Lazzeri on machine learning for time series forecasting as the main topic. This will include automated machine learning and deep learning for time series data forecasting. We will also talk about other emerging trends in machine learning, development and operations areas, including data science lifecycle. And if the time permits, at the end of the podcast, we will also discuss MLOps best practices.

Let me first introduce Dr. Lazzeri to our listeners. Dr. Francesca Lazzeri currently works as principal cloud advocate manager at Microsoft. She is an experienced scientist and a machine learning practitioner with over 12 years of both academic and industry experience. She is the author of a number of applications, including technology journals, conferences and books. Dr. Lazzeri currently leads an international team of data scientists, cloud advocates and AI developers at Microsoft. Before joining Microsoft organization, she was a research fellow at Harvard University in the technology and operations management unit. Dr. Lazzeri is no stranger to the InfoQ audience. She has already published articles on website, and also spoke recently at the QCon Plus virtual conference on MLOps topic.

Francesca, welcome to the podcast. Thank you for joining me today. Before we get started, do you have any additional comments about your recent research projects that you have been leading, that may be of interest to our readers?

Francesca Lazzeri: Hi everyone and thank you so much, Srini, for inviting me to this podcast. It’s just a pleasure for me to be here. I think that you gave our listeners a very good introduction. And today I would really like to talk about two main topics that are time series forecasting, and specifically how you can apply different machine learning techniques and deep learning approaches to two time series data. I’m sure that you have some questions for me on that topic.

And then some of the latest…

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