MLOps
Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A lightweight tool to get an AI Infrastructure Stack up in minutes not days. K3ai will take care of setup K8s for You, deploy the AI tool of your choice and even run your code on it.
TinyMaix is a tiny inference library for microcontrollers (TinyML).
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
A toolkit to run Ray applications on Kubernetes
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Standardized Serverless ML Inference Platform on Kubernetes
Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator suppor…
Public repo for DeepLearning.AI MLEP Specialization
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and l…
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
Infrastructures™ for Machine Learning Training/Inference in Production.
Distributed Asynchronous Hyperparameter Optimization in Python
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
☁️ Build multimodal AI applications with cloud-native stack
Open standard for machine learning interoperability
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
System design patterns for machine learning
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built wi…
NumPy aware dynamic Python compiler using LLVM