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PLEASE RSVP (https://www.eventbrite.com/e/deploy-a-machine-learning-model-and-data-pipeline-in-azure-cloud-tickets-50199984505) on Eventbrite also to expedite check-in
*We are hosting this event with the Phoenix DevOps Meetup(https://www.meetup.com/Phoenix-DevOps-Meetup/events/253982908/) so follow their group for more awesome DevOps events*
For the September DevOps meetup, will deploy a machine learning model and data pipeline using Azure Machine Learning Studio and Data Factory!
Azure offers amazing tools to deploy machine learning models and orchestrate data pipelines with very little code. However, the technology is not well documented there are many nuances. Learn how to get up and running with Machine Learning Studio and Data Factory in no time!
* Welcome & DevOpsDays Phoenix Update (5min)
* Deploy your machine learning model and data pipeline using Azure Machine Learning Studio and Data Factory (45min)
Speaker: Nadaa Taiyab, Data Scientist: (https://www.linkedin.com/in/nadaataiyab/)
In this talk, Nadaa will walk through how to train and deploy a machine learning model using Azure's Machine Learning Studio and how to use Data Factory to orchestrate a data pipeline. Machine Learning Studio offers a graphical interface for cleaning data and training models, using a combination of pre-built modules and custom code. Deploying a model and creating a web service can be done with just a click of a button. Testing the web service is also quite easy to do. However, there are certain important nuances that are not well documented in this process, which will be covered in detail.
Data Factory is a tool that allows you to orchestrate a data pipeline using a variety of storage and compute options. In this example, we will set up a daily batch predict on data from Blob storage. Again, there are several undocumented nuances that can cause great frustration for the beginner.
At the end of this talk, you will know the basics of ML Studio and Data Factory, how to run a simple daily batch prediction, and have the foundation you need to learn more about these technologies.
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Education - part-time and full-time training in web development, data science, and data engineering
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To learn more about Galvanize, visit galvanize.com.