Supply Chain, Healthcare, Insurance, and Finance often require highly accurate forecasting models in an enterprise large-scale fashion. With Azure Machine Learning combined with Azure Databricks, the scale and speed to large-scale many-models can be achieved and time-to-product decreases drastically. The better-together story poses an enterprise approach to AI/ML. Azure AutoML offers an elegant solution efficiently to build forecasting models on Azure Databricks compute solving sophisticated business problems. 

A presentation covers the Azure Machine Learning + Azure Databricks approach while the demo covers a hands-on business problem building a forecasting model in Azure Databricks using Azure Machine Learning. The AI/ML better-together story is elevated as MLFlow for Data Science Lifecycle Management and Hyperopt for distributed model execution completes AI/ML enterprise readiness for industry problems.

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Instructor's Bio

Moritz Steller

Applied AI Leader at Microsoft

Moritz is an Applied AI Leader at Microsoft with a long term focus on delivering AI/ML/Advanced Analytics, AI Business Development and Strategy, AI Scalability and Monetization, building AI Organizations, and Cloud Platform efforts over the last 12+ years. In particular, he specializes in NLP, Cognitive Engineering, Forecasting, and AI Automation across Supply Chain, Healthcare, Finance and Insurance. As an Data and AI Professional, his big passion is to revolutionize high-risk industries with innovative deep machine intelligence, deliver high scalable customer-centric solutions, transforming data into actions, and enabling the AI-driven enterprise. 

Outside of Microsoft, Moritz supports a NLP Healthcare company and is a career advisor for AI/ML at Harvard University. Moritz earned his Master’s Degree in Information Management Systems from Harvard University, Stanford’s Artificial Intelligence Professional Certificate, and several industry/solution certifications.


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    ON-DEMAND WEBINAR: Auto-train a time-series forecast model with AML & ADB

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