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What is Amazon SageMaker?

A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Amazon SageMaker is a tool in the Machine Learning as a Service category of a tech stack.

Who uses Amazon SageMaker?

105 companies reportedly use Amazon SageMaker in their tech stacks, including UpstageAI, Shelf, and technology.

171 developers on StackShare have stated that they use Amazon SageMaker.

Amazon SageMaker Integrations

Amazon EC2, TensorFlow, Caffe, Amazon Elastic Inference, and Amazon Timestream are some of the popular tools that integrate with Amazon SageMaker. Here's a list of all 20 tools that integrate with Amazon SageMaker.
Decisions about Amazon SageMaker

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon SageMaker in their tech stack.

Needs advice
Amazon SageMakerAmazon SageMaker

Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

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Harsh Solanki
Machine Learning Engineer at Quantiphi · | 4 upvotes · 23.4K views

We have to process the video stream to identify emotions. For which we need to use Amazon Rekognition/custom model on Amazon SageMaker. With Kinesis WebRTC Javascript SDK, currently, video can be streamed only into the kinesis signaling channel. Signaling channel data is available for streaming only and not processing (ML). So, how can we get real-time data for processing into Kinesis Streams from the frontend?

For streaming the video from frontend to backend into the Amazon Kinesis Video Streams for processing, we tested with Kinesis webRTC JavaScript SDK, and we are facing issues while implementing as mentioned above, so would Chime SDK serve as an alternative to this?

In Rekognition, "create-stream-processor" has a settings parameter. This currently only supports FaceSearch. We are looking to Detect and analyze faces. Is that possible with "create-stream-processor" in the Python SDK? Or do we have to use the Java SDK?

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Arthur Boghossian
DevOps Engineer at PlayAsYouGo · | 3 upvotes · 143.5K views

For our Compute services, we decided to use AWS Lambda as it is perfect for quick executions (perfect for a bot), is serverless, and is required by Amazon Lex, which we will use as the framework for our bot. We chose Amazon Lex as it integrates well with other #AWS services and uses the same technology as Alexa. This will give customers the ability to purchase licenses through their Alexa device. We chose Amazon DynamoDB to store customer information as it is a noSQL database, has high performance, and highly available. If we decide to train our own models for license recommendation we will either use Amazon SageMaker or Amazon EC2 with AWS Elastic Load Balancing (ELB) and AWS ASG as they are ideal for model training and inference.

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Amazon SageMaker's Features

  • Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
  • Train: one-click training, authentic model tuning
  • Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling

Amazon SageMaker Alternatives & Comparisons

What are some alternatives to Amazon SageMaker?
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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.
See all alternatives

Amazon SageMaker's Followers
276 developers follow Amazon SageMaker to keep up with related blogs and decisions.