A community of eningeers, researchers, students, and enthusiasts working to advance the knowledge and application of machine learning science.

Chicago ML hosts meetups, workshops, and symposiums to support learners and experts alike. We are an all-volunteer organization and rely on contributions of time and resources from our members and sponsoring organizations. Membership is free and without obligation—simply follow the link below to sign up on our Meetup page.

Chicago ML fosters an open and welcoming environment to everyone regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.

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Machine Learning & Data Science on AWS

Thursday, April 25, 2019 - 5:30 PM to 7:30 PM - CreditNinja

This is our second joint meetup with AWS Chicago User Group. We feature AWS EC2 for training a variety of models, demonstrating EC2’s ability to scale from small, less costly K80 GPUs up to fantastically powerful DGX-1 super computers!

While GPU accelerators on EC2 are not free, when used selectively they can be cost effective alternatives to buying and maintaining dedicated hardware.

Schedule:

5:30 - Welcome and pizza brought to you by CreditNinja 6:00 - Presentations 7:00 - Networking and delightful social exchange

This event is hosted by CreditNinja. The Blue Lacuna concept is a revolutionary approach to the work and play superstructure that focuses on collocating highly creative and entrepreneurial organizations with community-based programming.

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Guild AI workshop

Saturday, April 06, 2019 - 12:00 PM to 4:30 PM - IIT - Rettaliata Engineering

This is a deep dive into using Guild AI to build and optimize machine learning models.

For more information on Guild AI - see https://guild.ai

Guild AI is a tool for running, capturing, and tracking machine learning experiments. It’s used by researchers to systematically improve models using both intuition and AutoML methods such as Bayesian optimization.

The workshop will focus on model development and optimization. We will cover these topics:

  • Basic model training
  • Data preprocessing
  • Hyperparameter tuning
  • Comparing training results
  • Debugging models
  • Collaboration
  • Remote training

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Applied ML Engineering Workshop

Friday, March 29, 2019 - 1:00 PM to 5:00 PM - HERE Technologies

On Friday, March 29, Chicago ML and HERE Technologies are hosting a half day workshop on ML applied engineering. The half day is structured around four topics:

  • Identifying and communicating value to stake holders
  • ML model selection and training strategies
  • Automation and reproducibility
  • Deployment and production

Each topic is covered by one or more short presentations by experts followed by a panel discussion. Audience members participate through a question and answer session.

The purpose of the workshop is to share knowledge and experience within the community and to define an ongoing set of problems for further work.

If you would like to participate in the workshop as a presenter, please contact Garrett Smith <g@rre.tt>.

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AI Days Conference w/hosted track by Chicago ML

Tuesday, March 12, 2019 - 9:00 AM to 5:00 PM - Chicago AI Days

Chicago ML is partnering with AI Days to host an exciting one day conference on March 12 from 9:00 AM to 5:00 PM. Chicago ML is organizing the Emerging track in the afternoon, which will include talks by engineers and scientists highlighting their work in applied machine learning.

PLEASE NOTE: This is a paid event (though very affordable to community members) - you must register using the link below to attend.

AI Days is a Chicago conference that brings together practitioners and enthusiasts alike to learn and share about intelligent systems. This is a great opportunity to see what’s happened in Chicago across a range of business applications and projects.

For details and to register for the event, visit:

https://www.chicagoaidays.com

PLEASE NOTE (once again): This is a paid event and you must register using the link above. RSVPs through Meetup are disabled (you will be wait-listed to indicate that reservation is NOT in effect).

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Introduction to Machine Learning Engineering

Wednesday, February 27, 2019 - 6:00 PM to 8:00 PM - Traxonomy

What is machine learning engineering? It’s a new field that combines the science and methods of machine learning with traditional software and systems engineering. In this talk, Garrett Smith, founder of Guild AI, will present the challenges and opportunities for practitioners who want to apply machine learning to build exciting new applications. Garrett will cover the motivation for using machine learning in applications, tools of the trade, data analysis, model selection, model training, infrastructure, reproducibility, and production. All of these topics roll into complex, deep, and fascinating areas of work. If you’re a software programmer or engineer looking to get into machine learning, you’ll learn what’s involved in expanding your skillset into the realm of machine learning. If you’re a machine learning scientist or researcher, you’ll learn about the engineering processes needed to support your work in building applications.

Garrett Smith is founder of Guild AI, an open source toolkit for running, tracking, and comparing machine learning experiments. He is also co-organizer of Chicago ML and TensorFlow Chicago. He has over 20 years experience building software and systems. Prior to his work on Guild AI, Garrett built and ran CloudBees application platform, which hosted tens of thousands of enterprise applications at scale.

We will also host short presentations by members of the community on projects they’re working on:

  • Aman Agarwal - Use of Transfer Learning in Text Classification using FastAI library. Till now, transfer learning was mainly used in computer vision but FastAI is a wrapper library built around PyTorch that has brought this state-of-the-art approach in NLP tasks. I will be demonstrating the use of this library for US-Airline Sentiment Analysis.
  • Jay Rodge - Classifying Audio-MNIST using PyTorch.

Slides

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