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Amazon-sagemaker-examples Alternatives
Similar projects and alternatives to amazon-sagemaker-examples
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LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
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catboost
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aws-lambda-docker-serverless-inference
Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
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Popular-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
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AnnA_Anki_neuronal_Appendix
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better amazon-sagemaker-examples alternative or higher similarity.
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.
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Thesis Project Help Using SageMaker Free Tier
I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
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Sagemaker step scaling policy
I'm trying to define a step scaling policy for my sagemaker realtime endpoint, based on this example notebook. I understand that the step scaling policy defines thresholds to provision a different amount of instances, but I am confused because it doesn't seem to specify the metrics to track.
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Working On My Own Generative AI App, Taking ~10 sec to generate image.
Yeah man: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb
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Study Plan to pass exam AWS Machine Learning Specialty exam with tips and advice
It's time to get your hands dirty by solving some ML Use Cases of your own from AWS SageMaker Use Cases repo.
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Using AWS for Text Classification Part-1
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.
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[D] How to monitor NLP and Object Detection models on AWS Sagemaker?
We are kind of boxed into using Sagemaker at our organization and we need to do a POC for Sagemaker's model monitoring. We noticed that Sagemaker monitoring works best with models that use tabular data/features. There are a lot of example notebooks that demonstrate model monitoring capabilities, but all of the examples are based on tabular data. We are trying to apply Sagemaker's model monitoring and gather metrics from Data Quality, Model Quality, Bias Drift, Feature Attribution Drift, and Explainability and then push those metrics into CloudWatch, similar to what was done in these notebooks: https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_model_monitor .
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Migrate local Data Science workspaces to SageMaker Studio
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.
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
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:
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How InfoJobs (Adevinta) improves NLP model prediction performance with AWS Inferentia and Amazon SageMaker
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.
- amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
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A note from our sponsor - InfluxDB
www.influxdata.com | 4 May 2024
Stats
Basic amazon-sagemaker-examples repo stats
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9.1
2 days ago
awslabs/amazon-sagemaker-examples is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of amazon-sagemaker-examples is Jupyter Notebook.
Popular Comparisons
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