The easiest way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Multi-model Inference Graph/Pipelines, LLM/RAG apps, and more!
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Updated
May 24, 2024 - Python
The easiest way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Multi-model Inference Graph/Pipelines, LLM/RAG apps, and more!
Fast, private data connectors for AI ⚡️🤖
A library for authoring DLT pipelines via meta-programming patterns and deploying to Databricks workspaces.
Utilizing machine learning techniques, this project predicts vehicle prices based on features like age, fuel type, and more. Models include Linear Regression, Lasso Regression, and Random Forest. Dataset sourced from Kaggle.
Research on the topic "Transcoding JPEG images using prediction of DCT coefficients based on a neural network".
This is my Repository for Deep Learning Project and Works
ML Engineering with MS Azure
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.
This Repo contains a Box Detection Application capable of identifying box containers in conveyor belt pictures.
Study notes and demos.
⛰️ machine learning pipeline for disaster alert
Crack SWE (ML) / DS MAANG Interviews
Companion notebooks for blogs/tutorials on ML4Devs website.
Work-in-progress
UniTrends: Using Telegram API, Kafka, and AWS tools to analyze VISA group chats, refining my YouTube content strategy and gained 10k subscribers through data-driven insights.
An easy-to-use tool for making web service with API from your own Python functions.
Detecting News Generated by LLMs
A project to build an ETL pipeline and ML application to help respond to disaster events faster
Vehicle data classification (supervised, unsupervised learning)
Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng
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