|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Data Science Ipython Notebooks||25,668||2 months ago||34||other||Python|
|Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.|
|Awesome Kubernetes||14,349||a month ago||15||other||Shell|
|A curated list for awesome kubernetes sources :ship::tada:|
|Awesome Aws||11,773||a month ago||1||December 21, 2015||65||other||Python|
|A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.|
|Amazon Sagemaker Examples||9,078||2 days ago||873||apache-2.0||Jupyter Notebook|
|Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.|
|Metaflow||7,195||1||25||a day ago||103||December 04, 2023||308||apache-2.0||Python|
|:rocket: Build and manage real-life data science projects with ease!|
|Gluonts||3,932||16||a day ago||105||November 27, 2023||376||apache-2.0||Python|
|Probabilistic time series modeling in Python|
|Artificial Intelligence Deep Learning Machine Learning Tutorials||3,436||6 months ago||152||other||Python|
|A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.|
|Image Super Resolution||3,376||2 years ago||85||apache-2.0||Python|
|🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.|
|Data Science Best Resources||2,466||6 months ago||5||mit|
|Carefully curated resource links for data science in one place|
|Sagemaker Python Sdk||1,970||14||76||a day ago||554||November 30, 2023||471||apache-2.0||Python|
|A library for training and deploying machine learning models on Amazon SageMaker|
Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.
The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.
The Sagemaker Example Community repository are additional notebooks, beyond those critical for showcasing key SageMaker functionality, can be shared and explored by the commmunity.
The quickest setup to run example notebooks includes:
These example notebooks are automatically loaded into SageMaker Notebook Instances.
They can be accessed by clicking on the
SageMaker Examples tab in Jupyter or the SageMaker logo in JupyterLab.
Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
As of February 7, 2022, the default branch is named "main". See our announcement for details and how to update your existing clone.
These examples introduce SageMaker geospatial capabilities which makes it easy to build, train, and deploy ML models using geospatial data.
These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth.
These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.
These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful.
These examples introduce SageMaker Autopilot. Autopilot automatically performs feature engineering, model selection, model tuning (hyperparameter optimization) and allows you to directly deploy the best model to an endpoint to serve inference requests.
These examples provide quick walkthroughs to get you up and running with Amazon SageMaker's custom developed algorithms. Most of these algorithms can train on distributed hardware, scale incredibly well, and are faster and cheaper than popular alternatives.
The following provide examples demonstrating different capabilities of Amazon SageMaker RL.
These examples provide more thorough mathematical treatment on a select group of algorithms.
These examples provide and introduction to SageMaker Debugger which allows debugging and monitoring capabilities for training of machine learning and deep learning algorithms. Note that although these notebooks focus on a specific framework, the same approach works with all the frameworks that Amazon SageMaker Debugger supports. The notebooks below are listed in the order in which we recommend you review them.
These examples provide an introduction to SageMaker Distributed Training Libraries for data parallelism and model parallelism. The libraries are optimized for the SageMaker training environment, help adapt your distributed training jobs to SageMaker, and improve training speed and throughput. More examples for models such as BERT and YOLOv5 can be found in distributed_training/.
These examples provide an Introduction to Smart Sifting library. Smart Sifting is a framework to speed up training of PyTorch models. The framework implements a set of algorithms that filter out inconsequential training examples during training, reducing the computational cost and accelerating the training process. It is configuration-driven and extensible, allowing users to add custom logic to transform their training examples into a filterable format. Smart sifting provides a generic utility for any DNN model, and can reduce the training cost by up to 35% in infrastructure cost.
These examples provide an introduction to SageMaker Clarify which provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions.
These examples show you how to run R examples, and publish applications in RStudio on Amazon SageMaker to RStudio Connect.
These examples showcase unique functionality available in Amazon SageMaker. They cover a broad range of topics and utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.
These examples provide an introduction to how to use Neo to compile and optimize deep learning models.
These examples show you how to use SageMaker Processing jobs to run data processing workloads.
These examples show you how to use SageMaker Pipelines to create, automate and manage end-to-end Machine Learning workflows.
These examples show you how to train and host in pre-built deep learning framework containers using the SageMaker Python SDK.
These examples show you how to build Machine Learning models with frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK.
These examples show how to use Amazon SageMaker for model training, hosting, and inference through Apache Spark using SageMaker Spark. SageMaker Spark allows you to interleave Spark Pipeline stages with Pipeline stages that interact with Amazon SageMaker.
These examples show how to use Amazon SageMaker to read data from Amazon Keyspaces.
These example notebooks show you how to package a model or algorithm for listing in AWS Marketplace for machine learning.
Once you have created an algorithm or a model package to be listed in the AWS Marketplace, the next step is to list it in AWS Marketplace, and provide a sample notebook that customers can use to try your algorithm or model package.
These examples show you how to use model-packages and algorithms from AWS Marketplace and dataset products from AWS Data Exchange, for machine learning.
Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our contributing guidelines if you'd like to open an issue or submit a pull request.