Francesca Lazzeri will walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. You will use the training and deployment workflow for Azure Machine Learning service in a Python Jupyter notebook. You can then use the notebook as a template to train your own machine learning model with your own data.
Specifically, this tutorial will show how to generate stock market predictions with long short-term memory (LSTM) networks: LSTM models can use the history of a sequence of data and correctly predict what the future elements of the sequence are going to be. This is very helpful in many different financial use cases, for example, when you need to model stock prices correctly. Finally, you will learn how to deploy the model as a web service in Azure Container Instances (ACI). A web service is an image, in this case a Docker image, that encapsulates the scoring logic and the model itself. Each topic will include a lecture combined with hands-on exercises.
Francesca Lazzeri, PhD is AI & Machine Learning Scientist at Microsoft in the Cloud Developer Advocacy team. Francesca is passionate about innovations in big data technologies and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers a wide range of industries including energy, oil and gas, retail, aerospace, healthcare, and professional services.
Before joining Microsoft, she was 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 Business School, she worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.
Francesca holds a PhD in Economics & Management from Sant’Anna School of Advanced Studies and is currently Data Science Mentor for PhD and Postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding.
Wee Hyong Tok is a principal data science manager with Microsoft. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.
Krishna Anumalasetty is Principal Program Manager in Azure Machine Learning, Microsoft’s Cloud Machine Learning platform offering. He has been working as a program / product manager in Azure and the cloud services for the last 7 years with 4 of those in Machine Learning and Artificial Intelligence. Microsoft’s quest is to simplify ML & AI, enable customers to infuse AI in all Line Of Business Applications. Krishna has worked enabling enterprise customers with on-prem and cloud hybrid scenarios, scale up & out in cloud, security protections and easy to deploy ML models in the cloud scenarios. Krishna is a founding member of Microsoft’s AutoML team and helped bring AutoML to Microsoft’s customers. Krishna has graduated from Arizona State University with Masters in Computer Science.
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