Happy New Year!
TL;DR: I’m excited to announce a couple of new study group offerings in conjunction with our ever-expanding array of TWIML Community programs. One is focused on causality and the other on enterprise AI workflows.
We’re collaborating with research scientist and instructor Robert Ness to bring his course sequence, Causal Modeling in Machine Learning, to the TWIML Community. Causality has become a very hot topic in the ML/AI space. In fact, it’s come up in a good number of my recent conversations, like this one with Zach Lipton. One of the challenges facing those interested in learning about causality in ML is that most resources on the topic are geared towards the needs of statisticians or economists, versus those of data scientists and machine learning engineers.
Robert, a machine learning research scientist at ML startup Gamalon and an instructor at Northeastern University, designed his new causality courses to be more practical and accessible for data scientists and engineers. The first course in sequence is called Model-Based Thinking in Machine Learning and aims to help students develop universal mental model for data science and ML problem-solving while leaving them with a high-level understanding of causality in the context of ML.
Robert is simultaneously teaching these courses at the graduate level at Northeastern University and through his virtual AltDeep School of AI, and has agreed to host a study group for the course for the TWIML Community as well. The study group will meet at 8 am US Pacific Time on Saturdays. To get things kicked off, Robert and I will host an overview session on the course on Saturday, February 1st at that time.
ML and AI Platforms, and more broadly, strategies for developing and deploying machine learning and deep learning models in the enterprise, is another hot topic in our community and the broader industry. Heck, we held a whole conference on the topic.
Until now, there have been few formal courses for learning how to deploy real-world AI and ML workflows in the enterprise. IBM is has addressed this gap with the new IBM AI Enterprise Workflow Specialization it has recently published on Coursera.
The specialization consists of six courses which aim to progressively walk the learner through the experience of building and deploying a real-world enterprise AI solution, from establishing business priorities and a data pipeline through to deploying and managing your model in production.
I’ll personally be taking this six course sequence and hosting a study group for those of you who would like to join me.
If you’re doing, interested, or would like to learn more about how to do real-world machine learning in an enterprise environment, I’d encourage you to join me in taking this sequence. I’ll be hosting an informational session on the course and a study group in early February.
To join either of these study group or sign up for the respective overview sessions, first join the TWIML Community. Then, after joining our Slack via the invitation you’re receive via email, join the #ai_enterprise_workflow or #causality_course channels and say hi!