Need advice about which tool to choose?Ask the StackShare community!

Amazon SageMaker

283
271
+ 1
0
H2O

121
209
+ 1
8
Add tool

Amazon SageMaker vs H2O: What are the differences?

# Key Differences between Amazon SageMaker and H2O

Amazon SageMaker and H2O are both popular tools in the field of machine learning. Below are the key differences between the two platforms:

1. **Deployment Options**: Amazon SageMaker provides a fully managed platform for building, training, and deploying machine learning models in the cloud, simplifying the end-to-end machine learning process. In contrast, H2O primarily focuses on providing open-source machine learning algorithms and frameworks for data scientists to build models locally on their machines.

2. **Scalability**: Amazon SageMaker offers scalability and can handle large-scale model training and deployment with ease due to its integration with AWS cloud services. On the other hand, H2O is more limited in terms of scalability as it is designed for smaller-scale machine learning projects and may require additional setup for handling larger datasets.

3. **Model Selection**: Amazon SageMaker provides a wide range of built-in algorithms and pre-built models for various machine learning tasks, making it easier for users to select the most suitable model for their project. In contrast, H2O focuses on providing a diverse set of machine learning algorithms optimized for performance, giving users more control and customization options when building models.

4. **Integration**: Amazon SageMaker seamlessly integrates with other AWS services such as S3, IAM, and EC2, enabling easy data storage, security, and computational resources for machine learning tasks. On the other hand, H2O may require additional configurations and setup to integrate with external services or tools, making the process more complex for users.

5. **Collaboration**: Amazon SageMaker offers collaborative features such as notebook sharing and version control, allowing multiple data scientists to work together on the same projects effectively. In comparison, H2O lacks robust collaboration tools and may require external tools or platforms for team collaboration on machine learning projects.

6. **Cost**: While both Amazon SageMaker and H2O offer free versions or open-source options, the cost structure differs significantly. Amazon SageMaker's pricing is based on usage and resources consumed, including training hours and storage, while H2O's pricing model is typically based on enterprise subscriptions or support packages, making it more suitable for larger organizations with specific needs.

In Summary, Amazon SageMaker provides a scalable and fully managed platform with a wide range of built-in algorithms, seamless integration with AWS services, and collaborative features, whereas H2O focuses on providing open-source machine learning algorithms, customization options, and cost-effective solutions for smaller-scale projects.
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon SageMaker
Pros of H2O
    Be the first to leave a pro
    • 2
      Highly customizable
    • 2
      Very fast and powerful
    • 2
      Auto ML is amazing
    • 2
      Super easy to use

    Sign up to add or upvote prosMake informed product decisions

    Cons of Amazon SageMaker
    Cons of H2O
      Be the first to leave a con
      • 1
        Not very popular

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      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.

      What is H2O?

      H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

      Need advice about which tool to choose?Ask the StackShare community!

      Jobs that mention Amazon SageMaker and H2O as a desired skillset
      What companies use Amazon SageMaker?
      What companies use H2O?
      See which teams inside your own company are using Amazon SageMaker or H2O.
      Sign up for StackShare EnterpriseLearn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Amazon SageMaker?
      What tools integrate with H2O?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to Amazon SageMaker and H2O?
      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
      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.
      Kubeflow
      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
      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