For building machine learning (ML) workflows and pipelines on AWS
Create UIs for your machine learning model in Python in 3 minutes
Simple and distributed Machine Learning
Continuous Machine Learning | CI/CD for ML
Vector database for scalable similarity search and AI applications
.NET Standard bindings for Google's TensorFlow for developing models
A framework for real-life data science
Data science spreadsheet with Python & SQL
The data structure for multimodal data
Detecting silent model failure. NannyML estimates performance
Modest natural-language processing
Data science on data without acquiring a copy
Best practices on recommendation systems
Streamline your ML workflow
Serve machine learning models within a Docker container
Train machine learning models within Docker containers
A library of extension and helper modules for Python's data analysis
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
An API Oriented Open-source Python Framework for Unsupervised Learning
Explainability and Interpretability to Develop Reliable ML models
Build and deploy machine learning microservices
Build MLOps Pipelines in Minutes
An open-source NLP research library, built on PyTorch
Create software without writing a single line of code
Slides and Jupyter notebooks for the Deep Learning lectures