Put AI to Work
April 15-18, 2019
New York, NY

In-Person Training
Forecasting financial time series with deep learning on Azure

Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)

Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.

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.

What you'll learn, and how you can apply it

  • Learn how to set up your development environment, access and examine the data, train long short-term memory (LSTM) networks to generate stock market predictions, and review training results and register the best model
  • Learn how to set up your testing environment, retrieve the model from your workspace, test the model locally, deploy the model, and test the deployed model

This training is for you because...

  • You're a data scientist, software developer, data engineer, or financial data analyst who wants to use the Azure platform and Azure Machine Learning services to build financial time series forecasts.

Prerequisites:

  • Experience coding in Python
  • A basic understanding of machine learning and deep learning topics and terminology
  • Familiarity with time series forecasting (useful but not required)

Hardware and/or installation requirements:

  • A laptop with an up-to-date version of Edge or Chrome installed
  • An Azure account (If you don't have an Azure subscription, create a free account.)
  • An Azure Notebooks account

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.

Outline

  • Introduction to time series forecast
  • Introduction to neural networks for time series forecast
  • Azure Machine Learning services
  • Applied use case: Stock market predictions with LSTMs

About your instructors

Photo of Francesca Lazzeri

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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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.

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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.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

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Comments

Picture of Francesca Lazzeri
Francesca Lazzeri | SENIOR MACHINE LEARNING SCIENTIST
02/26/2019 6:18am EST

Hi Wendy, thanks for your message.
No, this training will be different from the online one, as it will be focused on deep learning with Azure ML service and we will use different use cases in the classroom.

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02/25/2019 11:56am EST

Will this training be the same as the Time Series Forecasting Online Training Recording on Safari Oreilly?