Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty 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’ll use the training and deployment workflow for the Azure Machine Learning service in a Python Jupyter notebook and then use the notebook as a template to train your own machine learning model with your own data. Each topic will include a lecture combined with hands-on exercises.
Specifically, you’ll learn 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’ll discover 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.
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.
Wee Hyong Tok is a principal data science manager with the AI CTO Office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. 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 a principal program manager for Azure Machine Learning at Microsoft. He’s worked as a program and product manager at Azure cloud services and AI org for the last eight years, enabling enterprise customers with on-premises and cloud hybrid scenarios, helping them scale up and out in the cloud and implement security protections and easy to deploy ML models in the cloud. Krishna is a founding member of Microsoft’s AutoML team. He holds a master’s degree in computer science from Arizona State University.
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