Khang Pham

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

A structured study guide for machine learning engineering interviews at big tech companies, covering LeetCode practice, SQL, statistics, and ML system design problems like building recommendation systems, with curated links to external resources.

Short description of LLM research and development

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This repository includes tutorials on how to use the TensorFlow estimator APIs to perform various ML tasks, in a systematic and standardised way

SWE-agent: Agent Computer Interfaces Enable Software Engineering Language Models

Craft Agent hackathon

Source code for Machine Learning book

RHadoop - [email protected]

No description.

Your personal Intern for engs

Reliability for Agent

Streamlit for Agentic Apps

A high-throughput and memory-efficient inference and serving engine for LLMs

NeMo Retriever extraction is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever extraction uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications.

Letta (formerly MemGPT) is a framework for creating LLM services with memory.

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OpenSSM: Safe, Reliable, and Trustable Small Specialist Models (SSMs) for Industrial AI Applications.

H1st AI solves three critical challenges in real-world data science: 1/CAN'T START: Industrial AI needs human insight. 2/CAN'T PROFIT: DS needs human tools. 3/CAN'T DEPLOY: AI needs human trust.

No description.

Implementation of Using Fast Weights to Attend to the Recent Past

Mirror of Apache Storm

Mirror of Apache Spark