I failed the Turing Test once, but that was many friends ago.
you-dont-need-a-bigger-boat. An end-to-end implementation of intent prediction with Metaflow and other cool tools
876MLSys-NYU-2022. Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
558recs-at-resonable-scale. Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow
239post-modern-stack. Joining the modern data stack with the modern ML stack
203foundation-models-for-dbt-entity-matching. Playground for using large language models into the Modern Data Stack for entity matching
110FREE_7773. Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).
96paas-data-ingestion. Ingesting data with Pulumi, AWS lambdas and Snowflake in a scalable, fully replayable manner
70tensorflow_to_lambda_serverless. Serve tensorflow models prediction from AWS lambda endpoints
56no-ops-machine-learning. A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.
37dag-card-is-the-new-model-card. Template-based generation of DAG cards from Metaflow classes, inspired by Google cards for machine learning models.
29retail-personalization-workshop. In-Session Personalization Workshop for eCommerce, April 2021, and the MICES Workshop in June 2021.
24MLSys-NYU-2023. Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2023.
22anki-drive-python-sdk. Python+node wrapper to read/send message from/to Anki Overdrive bluetooth vehicles.
18clothes-in-space. Personalization with deep learning in 100 lines of code
15pixel_from_lambda. Serve a 1x1 GIF pixel from an AWS lambda-powered endpoint
13spark_tree2lambda. Python micro-service to serve a decision tree trained with Spark through AWS Lambda
8intro-to-ai-agents-columbia-2025. Code snippets for my 2025 lecture at Columbia University, an introduction to AI Agents
8vibe-proving-with-llms. Training an LLM to generate mathematical proofs with a formal verifier in Python
7session-path. SessionPath is a deep learning model that provides personalized category suggestions for type-ahead APIs. This repo re-implements the original paper (https://arxiv.org/abs/2005.12781) leveraging Ludwig capabilities.
6tarski-2.0. Old-style computational semantics at the time of Python 3.6
5LLMs-to-Alloy. Example of LLM generated Alloy code for deductive reasoning from English descriptions.
5webppl_to_lambda_serverless. Deploying a webppl probabilistic program as an (AWS lambda) endpoint.
4magic-the-gpthering. Playground for generating cards in the style of "Magic The Gathering" using generative AI
4On-the-plurality-of-graphs. WIP code for the "on the plurality of graphs" paper
3how-much-is-a-billion. Generating meaningful perspectives with NLP and Probabilistic Programming.
2jacopotagliabue.github.io. Personal website
2