The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and much more!
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Updated
Oct 31, 2024 - Python
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and much more!
Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng
make LLMs improve through experience
A "production-ready" simple project template to quickly start an Artificial Intelligence (AI), Machine Learning (ML) and/or Data Science (DS) project with basic files, branches and directory structure.
Scaffolding for serving ml model APIs using FastAPI
Kafka variant of the MLOps Level 1 stack
Companion notebooks for blogs/tutorials on ML4Devs website.
Build end-to-end Machine Learning pipeline to predict accessibility of playgrounds in NYC
Fast, private data connectors for AI ⚡️🤖
Study notes and demos.
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
Code for "Training models when data doesn't fit in memory" post
Vehicle data classification (supervised, unsupervised learning)
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype…
An easy-to-use tool for making web service with API from your own Python functions.
This Repo contains a Box Detection Application capable of identifying box containers in conveyor belt pictures.
The work shown in this repository is part of the Udacity scholarship program in collaboration with Microsoft for Machine Learning Engineer Nanodegree.
Crack SWE (ML) / DS MAANG Interviews
"When in doubt, use brute force." - Ken Thompson
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