Productionizing Time-Series Workloads at Siemens Energy with Edgar Bahilo Rodriguez

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we close out our re:Invent series joined by Edgar Bahilo Rodriguez, Lead Data Scientist in the industrial applications division of Siemens Energy.

Edgar spoke at this year’s re:Invent conference about Productionizing R Workloads, and the resurrection of R for machine learning and productionalization. In our conversation with Edgar, we explore the fundamentals of building a strong machine learning infrastructure, and how they’re breaking down applications and using mixed technologies to build models.

We also discuss their industrial applications, including wind, power production management, managing systems intent on decreasing the environmental impact of pre-existing installations, and their extensive use of time-series forecasting across these use cases.

Thanks to our Sponsor!

Before we get into today’s episode, I want to send a huge thanks to our friends at Amazon Web Services for their support of the podcast and sponsorship of this year’s re:Invent series.

AWS offers a vast array of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist, and expert practitioner. It’s extensive set of machine learning services at all three layers of the technology stack offers a broad array capabilities including contact center intelligence, dev/ops tools, industrial machine learning, enterprise search, health analytics, and much more. And, Amazon SageMaker – one of the fastest growing services in AWS history – helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for ML. To learn more about AWS machine learning services, and how they’re helping tens of thousands of customers accelerate their machine learning journeys, visit

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“More On That Later” by Lee Rosevere licensed under CC By 4.0

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