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Amazon SageMaker Pipelines

First purpose-built CI/CD service for machine learning
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What is Amazon SageMaker Pipelines?

It is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
Amazon SageMaker Pipelines is a tool in the Machine Learning Tools category of a tech stack.

Amazon SageMaker Pipelines Integrations

Amazon SageMaker Pipelines's Features

  • Compose, manage, and reuse ML workflows
  • Choose the best models for deploying into production
  • Automatic tracking of models
  • Bring CI/CD to machine learning

Amazon SageMaker Pipelines Alternatives & Comparisons

What are some alternatives to Amazon SageMaker Pipelines?
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
Continuous integration and delivery platform helps software teams rapidly release code with confidence by automating the build, test, and deploy process. Offers a modern software development platform that lets teams ramp.
Travis CI
Free for open source projects, our CI environment provides multiple runtimes (e.g. Node.js or PHP versions), data stores and so on. Because of this, hosting your project on means you can effortlessly test your library or applications against multiple runtimes and data stores without even having all of them installed locally.
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
GitLab CI
GitLab offers a continuous integration service. If you add a .gitlab-ci.yml file to the root directory of your repository, and configure your GitLab project to use a Runner, then each merge request or push triggers your CI pipeline.
See all alternatives
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