![]() ![]() When should I build my own algorithm container? However, this example remains relevant as an outline for bringing in other libraries to SageMaker as your own container. We recommend the pre-built container be used for almost all cases requiring a scikit algorithm. *Note:* SageMaker now includes a pre-built scikit container. This notebook will guide you through an example that shows you how to build a Docker container for SageMaker and use it for training and inference.īy packaging an algorithm in a container, you can bring almost any code to the Amazon SageMaker environment, regardless of programming language, environment, framework, or dependencies. With Amazon SageMaker, you can package your own algorithms that can than be trained and deployed in the SageMaker environment. ![]() Part 2: Using your Algorithm in Amazon SageMaker.Testing your algorithm on your local machine or on an Amazon SageMaker notebook instance.Part 1: Packaging and Uploading your Algorithm for use with Amazon SageMaker.When should I build my own algorithm container?.Get started with SageMaker Feature Store.Amazon Keyspaces (for Apache Cassandra).SageMaker Algorithms with Pre-Trained Model Examples by Problem Type.Pipelines with NLP for Product Rating Prediction.Music Streaming Service: Customer Churn Detection.Understanding Trends in Company Valuation with NLP.Iris Training and Prediction with Sagemaker Scikit-learn.Regression with Amazon SageMaker XGBoost algorithm.Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines.Fairness and Explainability with SageMaker Clarify.Amazon SageMaker Multi-hop Lineage Queries.Use SageMaker Batch Transform for PyTorch Batch Inference.Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo.Run a SageMaker Experiment with MNIST Handwritten Digits Classification.Train a SKLearn Model using Script Mode.Hyperparameter Tuning with the SageMaker TensorFlow Container.Distributed Data Processing using Apache Spark and SageMaker Processing.
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