Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. (by awslabs)

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amazon-sagemaker-examples reviews and mentions

Posts with mentions or reviews of amazon-sagemaker-examples. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-27.
  • Using AWS for Text Classification Part-1
    2 projects | | 27 Aug 2022
    Additionally, you can easily deploy pretrained fastText models on their own to live SageMaker endpoints to compute embedding vectors on the fly for use in relevant word-level tasks. See the following GitHub example for more details.
  • Migrate local Data Science workspaces to SageMaker Studio
    3 projects | | 8 Aug 2022
    Amazon SageMaker provides XGBoost as a built-in algorithm and data science team decided to use it and re-train the model. So, data scientists just need to call built-in version and provide path to data on S3, more detailed description can be found in documentation. Example notebook can be found here.
  • What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
    3 projects | | 28 Jun 2022
    Amazon SageMaker provides four new tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular and TabTransformer. These popular, state-of-the-art algorithms can be used for both tabular classification and regression tasks. They are available through the SageMaker JumpStart UI inside of SageMaker Studio, as well as through python code using SageMaker Python SDK. To learn how to use these algorithms, you can find SageMaker example notebooks below:
  • How InfoJobs (Adevinta) improves NLP model prediction performance with AWS Inferentia and Amazon SageMaker
    2 projects | | 9 Jun 2022
    In this section, we go through an example in which we show you how to compile a BERT model with Neo for AWS Inferentia. We then deploy that model to a SageMaker endpoint. You can find a sample notebook describing the whole process in detail on GitHub.
  • NLP@AWS Newsletter 04/2022
    2 projects | | 4 Apr 2022
    Train EleutherAI GPT-J using SageMaker EleutherAI released GPT-J 6B as an open-source alternative to OpenAI's GPT-3. EleutherAI’s goal was to train a model that is equivalent in size to GPT⁠-⁠3 and make it available to the public under an open license and has since gained a lot of interest from Researchers, Data Scientists, and even Software Developers. This notebook shows you how to easily train and tune GPT-J using Amazon SageMaker Distributed Training and Hugging Face on NVIDIA GPU instances.
  • AWS - NLP newsletter November 2021
    2 projects | | 24 Nov 2021
    Amazon SageMaker Asynchronous Inference with Hugging Face Model Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming requests and processes them asynchronously. SageMaker currently offers two inference options for customers to deploy machine learning models: 1) a real-time option for low-latency workloads 2) Batch transform, an offline option to process inference requests on batches of data available upfront. Real-time inference is suited for workloads with payload sizes of less than 6 MB and require inference requests to be processed within 60 seconds. Batch transform is suitable for offline inference on batches of data. This notebook provides an introduction on how to use the SageMaker Asynchronous inference capability with Hugging Face models. This notebook will cover the steps required to create an Asynchronous inference endpoint and test it with some sample requests.
  • I can't find a way to use pytorch for machine learning
    3 projects | /r/pytorch | 8 Feb 2021
  • Sorting my socks with deep learning — Part 1
    4 projects | | 7 Jan 2021
    A more extensive explanation here
  • A note from our sponsor - WorkOS | 17 Apr 2024
    The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →


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