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 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.
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