[In-Person & Online] Data Workshop: No Code AI for Data Engineers and Operators

We are excited to partner with Ikigai labs to host the in-person meetup in MIT campus.

Agenda:

• 5PM~5:30PM: check in, networking with food/drink (in-person)
• 5:30PM~7:30PM: Tech talks/workshops (in-person & online)
• 7:30PM~8PM: Q&A and mingle

Note:

• Livestream/online will start at 5:30PM EST (zoom link to join: https://us02web.zoom.us/j/83685164584?pwd=Y2pVQkdienZvSUdPdWE4TEQxZFZHQT09)
• In-person conference room: Building 32, Room number 123 (on the ground floor) MIT Campus
• You need to request MIT visitor pass before entering the building: https://visitors.mit.edu/?event=35f2b5f5-7b7f-4eb5-9712-c2c707b57fe6

Workshop: No Code AI for Data Operators
Abstract: Data professionals (analysts, scientists, operators, …) utilize data to extract insights from it and subsequently make decisions that impact day-to-day operations as well as long-term strategy for organizations. The process of going from data to insights and using decisions typically involves (a) extracting data from varied structured and unstructured sources; (b) normalizing, cleaning, and stitching such varied data sources to obtain “ground truth”; (c) extracting structure within data, interacting with it, visualizing it to obtain insights; (d) predicting, optimization, doing scenario analysis to make decisions, and (e) automating all of the above while allowing for human in the loop intervention.

In this hands-on workshop, we shall discuss all of these with the help of illustrative datasets in a low-code / no-code AI environment.

What is Needed:

• A good, functional device that can connect to the internet via browser (preferably Chrome).
• Ability to do basic “spreadsheets” operations.
• And most importantly, a positive attitude and curiosity to learn.

Speaker: Devavrat Shah
Devavrat Shah is an Andrew (1956) and Erna Viterbi professor of Computer Science and AI at MIT since 2005 where he founded MIT's Statistics and Data Science Center and currently directs Deshpande Center for Tech Innovation. Previously, he co-founded Celect, focused on inventory optimization using AI (acquired by Nike in 2019). Currently, he serves as the CTO of Ikigai Labs which he co-founded in 2019, with the mission of building self-driving organization by empowering data business operators to make data-driven decisions with ease of spreadsheets.

Sponsors:
Ikigai is a no-code BI platform that helps business users turn their data into forward-looking actions, alters, and automation. As the only commercially available product built upon the cutting-edge MIT research on AI and machine learning, Ikigai is uniquely positioned to help operational teams improve the speed and accuracy of their decisions under uncertainty and constant change, ultimately increasing the ROI for their business. Learn more at ikigailabs.io