aws / amazon-sagemaker-examplesLinks
Example π Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using π§ Amazon SageMaker.
β10,577Updated 3 months ago
Alternatives and similar repositories for amazon-sagemaker-examples
Users that are interested in amazon-sagemaker-examples are comparing it to the libraries listed below
Sorting:
- A library for training and deploying machine learning models on Amazon SageMakerβ2,169Updated this week
- AI and Machine Learning with Kubeflow, Amazon EKS, and SageMakerβ3,391Updated 11 months ago
- AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.β1,081Updated this week
- An open source python library for automated feature engineeringβ7,481Updated 2 weeks ago
- A hyperparameter optimization frameworkβ12,246Updated this week
- Automated Machine Learning with scikit-learnβ7,877Updated last week
- Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoftβ4,217Updated 3 months ago
- A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learningβ7,006Updated 2 weeks ago
- A scikit-learn compatible neural network library that wraps PyTorchβ6,063Updated last week
- A guideline for building practical production-level deep learning systems to be deployed in real world applications.β4,476Updated 3 weeks ago
- Visualizations for machine learning datasetsβ7,380Updated 2 years ago
- A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.β9,928Updated last month
- Distributed Asynchronous Hyperparameter Optimization in Pythonβ7,440Updated last month
- Train machine learning models within a π³ Docker container using π§ Amazon SageMaker.β516Updated 3 weeks ago
- Best Practices on Recommendation Systemsβ20,418Updated last week
- Notebooks and examples on how to onboard and use various features of Amazon Forecast.β526Updated 2 years ago
- Hummingbird compiles trained ML models into tensor computation for faster inference.β3,446Updated 2 months ago
- NYU Deep Learning Spring 2020β6,766Updated 2 weeks ago
- A system for quickly generating training data with weak supervisionβ5,877Updated last year
- A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other maβ¦β8,455Updated this week
- Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce,β¦β28,321Updated last year
- A curated list of references for MLOpsβ13,199Updated 7 months ago
- Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentationβ3,175Updated 2 months ago
- βΎοΈ CML - Continuous Machine Learning | CI/CD for MLβ4,112Updated last month
- 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.β12,991Updated 2 weeks ago
- Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.β14,519Updated this week
- Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017β1,383Updated 3 months ago
- H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Fβ¦β7,217Updated this week
- AutoML library for deep learningβ9,244Updated 6 months ago
- This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.β10,357Updated 4 years ago