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April 15-18, 2019
New York, NY
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In-Person Training
Forecasting Financial Time Series with Deep Learning on Azure

Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)
Monday, April 15 & Tuesday, April 16,
9:00am - 5:00pm
Implementing AI, Models and Methods
Location: Green Room
Secondary topics:  Deep Learning and Machine Learning tools, Financial Services, Models and Methods, Temporal data and time-series

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 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.

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

  • Set up your development environment
  • Access and examine the data
  • Train long short-term memory (LSTM) networks to generate stock market predictions
  • Review training results and register the best model
  • Set up your testing environment
  • Retrieve the model from your workspace
  • Test the model locally
  • Deploy the model
  • Test the deployed model

This training is for you because...

You are a data scientist, software developer, data engineer, or financial data analyst, who wants to use Azure platform and Azure Machine Learning services for machine learning and building financial time series forecasts.


  • Experience coding in Python o A basic understanding of machine learning and deep learning topics and terminology
  • Familiarity with Time Series Forecast (useful but not required)

Hardware and/or installation requirements:

  • A laptop with an up-to-date version of Edge or Chrome.
  • If you don’t have an Azure subscription, create a free account.

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 inĀ Azure Container InstancesĀ (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.


  1. Introduction to Time Series Forecast
  2. Introduction to Neural Networks for Time Series Forecast
  3. Azure Machine Learning Services
  4. Applied Use Case: Stock Market Predictions with LSTMs

About your instructors

Photo of Francesca Lazzeri

Francesca Lazzeri, PhD is AI & Machine Learning Scientist at Microsoft in the Cloud Developer Advocacy team. Francesca is passionate about innovations in big data technologies 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.

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 and is currently Data Science Mentor for PhD and Postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding.

Social links:


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Wee Hyong Tok is a principal data science manager with Microsoft. 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 Principal Program Manager in Azure Machine Learning, Microsoft’s Cloud Machine Learning platform offering. He has been working as a program / product manager in Azure and the cloud services for the last 7 years with 4 of those in Machine Learning and Artificial Intelligence. Microsoft’s quest is to simplify ML & AI, enable customers to infuse AI in all Line Of Business Applications. Krishna has worked enabling enterprise customers with on-prem and cloud hybrid scenarios, scale up & out in cloud, security protections and easy to deploy ML models in the cloud scenarios. Krishna is a founding member of Microsoft’s AutoML team and helped bring AutoML to Microsoft’s customers. Krishna has graduated from Arizona State University with Masters in Computer Science.

Conference registration

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Picture of Francesca Lazzeri
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.

Wendy Wang |
02/25/2019 11:56am EST

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