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No Code AI for Data Operators and Entrepreneurs
By Boston New Technology

In this hands-on workshop, the Ikigai Labs team discusses how to analyze, predict and automate complex datasets in low code.

About this event

Hybrid event- food and drinks provided in person

Abstract: Data professionals (analysts, scientists, operators, …) utilize data to extract insights from it and subsequently to 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 involve (a) extracting data from varied structured and unstructured sources; (b) normalizing, cleaning, 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.


BIO 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.

For more details about him:


Ikigai is a no-code BI platform that helps business users turn their data into forward-looking actions, alters, and automations. 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.

As a cloud-native single platform, Ikigai seamlessly integrates with your existing tech stack and stitches together any data sources to streamline end-to-end data-driven processes, combining data analytics, visualizations, and automation with its unique technology, such as data reconciliation (DeepMatchTM) or multivariant time series forecasting, and making them accessible for any data professional.

Ikigai's top use cases include:

- Financial reconciliations (i.e. payments / accounts receivables / payables, claims auditing, customer identity)

- Supply chain forecasting (i.e demand planning, sales forecasting, cash-flow forecasting)

- Operations optimization / what-if analysis via reinforcement learning (i.e. production planning, cash optimization, sales recommendations).

Learn more at

Location: 32-155 [it’s on ground floor of building 32 (Stata Center)]

Access: MIT requires signing up for TIM ticket to access venue. Below are instructions that should accompany the announcement.

IMPORTANT: COVID REGISTRATION. To enter the building everyone will need a QR pass code on their phone, which will be scanned at the entrance. This involves two steps:

1/ Any time before the event, you must register and attest to being vaccinated. The link is:

2/ on the morning of June 21st, use the same link, and fill out the “daily attestation” to confirm you have no recent symptoms or exposure. Only at this point will you get a QR code which will allow you entry to the Center.

NOTE: after the 'daily attestation' it takes approximately 30 minutes to get the code, so best to do this at home before coming to MIT.

Food and drinks provided at venue.

June 21
6:00pm - 8:00pm (EDT)
MIT Stata Center
32 Vassar Street
Cambridge, Massachusetts United States
This is an online event at MIT Stata Center Go to event website