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

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

Francesca Lazzeri (Microsoft)
Monday, March 25 & Tuesday, March 26, 9:00am - 5:00pm
Secondary topics:  Deep Learning, Financial Services, Temporal data and time-series analytics

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 walks 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.)

Francesca Lazzeri walks 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 instructor

Photo of Francesca Lazzeri

Francesca Lazzeri, PhD is Machine Learning Scientist at Microsoft in 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 work on these issues covers a wide range of industries including energy, oil and gas, retail, aerospace, healthcare, and professional services.

Francesca periodically teaches applied analytics and machine learning classes at universities in USA and Europe.

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. She is also Data Science mentor for PhD and Postdoc students at the Massachusetts Institute of Technology, and keynote and featured speaker at academic and industry conferences – where she shares her knowledge and passion for AI, machine learning, and coding.

Twitter for frlazzeri

Conference registration

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