noodlefrenzy

node-amqp10

amqp10 is a promise-based, AMQP 1.0 compliant node.js client

easy-tensorflow-multimodel-server

Simple to run server for multiple TensorFlow Object Detection models

promptgen

CLI for managing and generating Foundation Model prompts

NikkeiAISummit2019

Deck and supporting content for talk at Nikkei AI Summit - 2019.04.22

SocialGoodAtCloudScale

Social Good at Cloud Scale - a talk at MLPrague 2018, and supporting materials.

ImageRecognitionInKeras

A few simple scripts to help you train and evaluate Transfer Learning-based custom image recognition models.

MLTokyo2019Talk

Presentation and supporting materials for my talk to the MLTokyo group in November of 2019

node-cerulean

Wrappers, utilities, etc. for making working with Azure and Azure Storage even easier in Node.js

StrataDataLondon2018

Content for the talk Elena Terenzi and I gave at Strata Data in London on 24-May-2018.

chatrunner

aml-keras-image-recognition

aml-tf-object-detection-deployment

A sample Azure Machine Learning project for creating the Docker container with a pre-trained object detection model and deploying it as API

azure-docs

bingtilesystem

TypeScript/Node bing tile system stuffs

bt

bt - flexible backtesting for Python

code-with-engineering-playbook

This is the playbook for "code-with" customer or partner engagements

covid19japan

edgellm

LLMs on the Edge

fastai

The fast.ai deep learning library, lessons, and tutorials

ffn

ffn - a financial function library for Python

langchain-ts-conversations

Testing ability of models to converse with themselves and self-correct

LLM-Augmenter

llm-challenger

Using LangChain.js to build intuition on how LLMs can self-correct and self-regulate.

MLOpsManufacturing

Demonstrate samples and good engineering practice for operationalizing machine learning solutions.

noodlefrenzy.github.io

Public site for Michael Lanzetta (noodlefrenzy), a Software Engineer, Data Scientist, and Manager at Microsoft.

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

PythonDataScienceHandbook

Jupyter Notebooks for the Python Data Science Handbook

semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps

stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

tuned-lens

Tools for understanding how transformer predictions are built layer-by-layer