One of the silver linings to an otherwise crazy year was that I spent a lot of time at a desk that would have otherwise been spent in planes, trains, and automobiles. As a result, Team TWIML got quite a lot done this year!
I thought I’d share a few of the accomplishments we’re most proud of this year.
Expansion of Our Education & Community Programs
Thinking back to our team retreat in January (good times!) a big part of what we wanted to accomplish this year was to broaden our education and community offerings, and to further the vision of the TWIML Community as a place for those interested in ML and AI to advance their skills and knowledge.
In January we partnered with instructor Robert Ness to launch his Causal Modeling in Machine Learning course to rave reviews. We’ve since delivered it to two additional cohorts, and we’re super excited to work with Robert to launch two new courses in January. We also worked with Luigi Patruno to launch his first course on Amazon SageMaker this year.
Our community stepped up in a big way this year as well. This year we held more study groups than ever— sixteen in total—including standbys like the Fast.ai Practical Deep Learning for Coders course that we’ve run for several years now, as well as a host of new offerings like the aforementioned AI Enterprise Workflow group, and groups focused on topics like Deep Reinforcement Learning, Natural Language Processing, Kaggle, and more.
Leaning into Video
Early on in the pandemic we realized that folks would be spending a lot more time in front of their computers than usual, and we decided to help them take advantage of this opportunity by leaning into video.
We started with our first video interview back in April (Google’s Quoc Le) and at this point I can’t remember the last audio-only interview I’ve done.
We also added online panel discussions to our regular repertoire this year. In an effort to support our community during the pandemic we took on subjects like Responsible Data Science in the Fight Against COVID-19, and Advancing Your Data Science Career During the Pandemic. We’ve since broadened our coverage to include practical and technical topics like the ML Programming Language Un-Debate, the Model Explainability Forum, and Feature Stores for Accelerating AI Development.
If you missed any of these, you can watch them on our YouTube channel. Please take a moment to subscribe here!
TWIMLfest: A Virtual AI Festival
We were really inspired by our community this year, and the passion and zeal they continued to bring to the study and advancement of ML and AI, even during a pandemic. To celebrate them and their accomplishments, we launched TWIMLfest, a global celebration of the TWIML Community.
TWIMLfest was a virtual festival spanning three weeks, dedicated to connection, collaboration, fun, and learning.
While we originally envisioned something much smaller, the event ultimately spanned 3 weeks and offered 40 sessions, hosted 70+ speakers, and saw over 1,700 members of our community register! Some of the highlights include my keynote Interview with Sal Khan, the panel discussion on Accessibility and Computer Vision, and the Coded Bias Film Screening + Director Q&A.
Stacking the Bricks
In addition to these programs, we also laid the groundwork for a lot of exciting work that will come to fruition in early 2021, including our AI Solutions Guide and the TWIMLcon conference, and we’re within spitting distance of achieving 10 million downloads, a milestone we expect to hit early next year. We’ve also been working behind the scenes to fine-tune a lot of what we do as a company, an ongoing effort that will allow us to elevate our for ML/AI programming and events once again.
We can’t wait to see you all in 2021! From the entire TWIML team, we wish you happy holidays as we celebrate the end of this wild year.