Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Forecasting financial time series with deep learning on Azure

Francesca Lazzeri (Microsoft), Jen Ren (Microsoft)
Monday, March 25 & Tuesday, March 26, 9:00am - 5:00pm

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 and Jen Ren 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.)
  • Azure Notebooks account: https://aka.ms/AzureNB

Francesca Lazzeri and Jen Ren 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.

Twitter for frlazzeri

Jen Ren is a program manager at Microsoft, focused on creating data wrangling tools for AI datasets. She studied health policy and computer science at Stanford University, where she was also part of the Data Challenge Lab.

Twitter for ren_jennifer

Conference registration

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