sep 30 - okt 02 | 2019 Ede, Netherlands
09:00 - 17:00

Cloud scaling machine learning

Description

This workshop is designed to get attendees up to speed on the rudiments of modern machine learning at cloud scale. Attendees can expect to learn the following:

  • An Overview of Machine Learning
  • Popular Machine Learning Frameworks
  • Azure Machine Learning service
  • Experimentation, Model Management, and Deployment
  • Hyperparameter optimization, automatic machine learning, and the machine learning pipeline This one-day workshop will be divided into 4 distinct segments with a lecture and lab portion.
Agenda

Hour 1 – An Overview of Machine Learning

This section will introduce the foundational concepts of machine learning primarily focusing on techniques for supervised learning. This section will delve into practical approaches for creating local machine learning models using both scikit-learn and PyTorch.

Hour 2 – Hands on with scikit-learn and TensorFlow

This hands-on lab includes setting up a local development environment as well as two machine learning problems attendees will solve with scikit-learn and TensorFlow

Hour 3 – Understanding Azure Machine Learning service

This portion of the workshop will introduce attendees to the basics of Azure Machine Learning service. Concepts such as a workspace, compute environment, data stores, experiments, etc. will be introduced in preparation for a machine learning experiment in the cloud.

Hour 4 – Your First Machine Learning Experiment in the Cloud

The goal of this lab is to create an Azure Machine Learning (AML) workspace and compute environment in order to run an experiment in the cloud. Attendees will essentially be lift-and-shifting Hour 2 code and running it in the cloud.

Hour 5 – Advanced Experimentation Techniques in Azure Machine Learning service

This section of the workshop will delve into three advanced techniques available for experimentation in Azure Machine Learning service: hyperparameter tuning, automatic machine learning, and pipelines. The first two features are designed to create agility in the data science process by automating several repetitive tasks associated with starting a new project. The last feature (pipelines) is designed to simplify complex multi-step machine learning scenarios by discretizing disparate steps without having to sacrifice speed and efficiency.

Hour 6 – Hands-on Advanced Experimentation in Azure Machine Learning service

The lab portion will contain exercises designed to help attendees better understand automatic hyperparameter tuning, automatic machine learning, and Azure Machine Learning pipelines.

Hour 7 – The Intersection of Data Science and DevOps

This section will introduce attendees to 3 features in Azure Machine Learning service designed to manage the artifacts associated with machine learning: model, image, and service management. Attendees will also learn what to do when things change. What should happen when data has changed and machine learning models no longer work? What if there’s a better machine learning model for the problem?

Hour 8 – Automating your Machine Learning Pipeline in the Cloud

The goal of this hands-on lab is to promote machine learning models created during the workshop all the way to production.

Prerequisites

There will be a page with the required pre-requisites for the workshop. It will include instructions for preparing the proper Python environment.

seth-juarez.jpg
Seth Juarez & Cassie Siljander

My name is Seth Juarez. I currently reside near Redmond, Washington and am a Cloud Developer Advocate focusing on Artificial Intelligence, Machine Learning, and Quantum Computing.
I received my Bachelor’s Degree in Computer Science at UNLV with a Minor in Mathematics and completed my Masters Degree at the University of Utah in the field of Computer Science. I currently am interested in Artificial Intelligence (specifically in the realm of Machine Learning) and Quantum Computing.