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Amazon SageMaker VS Saturn Cloud

Compare Amazon SageMaker VS Saturn Cloud and see what are their differences

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Amazon SageMaker logo Amazon SageMaker

Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Saturn Cloud logo Saturn Cloud

ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.
  • Amazon SageMaker Landing page
    Landing page //
    2023-03-15
  • Saturn Cloud Homepage
    Homepage //
    2024-03-11

Saturn Cloud is an award-winning ML platform with 75,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment.

Data scientists and analysts work best using the tools they want to use. You can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.

Amazon SageMaker features and specs

  • Fully Managed Service
    Amazon SageMaker is a fully managed service that eliminates the heavy lifting involved with setting up and maintaining infrastructure for machine learning. This allows data scientists and developers to focus on building and deploying machine learning models without worrying about underlying servers or infrastructure.
  • Scalability
    Amazon SageMaker provides scalable resources that can automatically adjust to the needs of your workload, ensuring that you can handle anything from small-scale experimentation to large-scale production deployments.
  • Integrated Development Environment
    SageMaker includes a built-in Jupyter notebook interface, which makes it straightforward for data scientists to write code, visualize data, and run experiments interactively without leaving the platform.
  • Support for Popular Machine Learning Frameworks
    SageMaker supports popular frameworks such as TensorFlow, PyTorch, Apache MXNet, and more. It also provides pre-built algorithms that can be used out-of-the-box, offering flexibility in choosing the right tool for your ML tasks.
  • Automatic Model Tuning
    SageMaker includes hyperparameter tuning capabilities that automate the process of finding the best set of hyperparameters for your model, thus saving significant time and computational resources.
  • Advanced Security Features
    SageMaker integrates with AWS Identity and Access Management (IAM) for fine-grained access control, supports encryption of data at rest and in transit, and complies with various security standards, ensuring that your machine learning projects are secure.
  • Cost Management
    With SageMaker, you only pay for what you use. This pay-as-you-go pricing model allows for better cost management and optimization, making it a cost-effective solution for various machine learning workloads.

Possible disadvantages of Amazon SageMaker

  • Complexity for New Users
    The plethora of features and options available in SageMaker can be overwhelming for beginners who are new to machine learning or the AWS ecosystem. It might require a steep learning curve to become proficient in using the platform effectively.
  • Vendor Lock-In
    Using Amazon SageMaker ties you to the AWS ecosystem, which can be a disadvantage if you want flexibility in switching between different cloud providers. Migrating models and workflows from SageMaker to another platform could be challenging.
  • Cost Management Challenges
    While SageMaker offers a pay-as-you-go pricing model, the costs can quickly add up, especially for large-scale or long-running tasks. It may require diligent monitoring and optimization to avoid unexpectedly high bills.
  • Resource Limitations
    While SageMaker is highly scalable, there are certain resource limits (like instance types and quotas) that might be restrictive for very high-demand or specialized machine learning tasks. These limits could potentially hinder the flexibility you get from an on-premises or custom deployed solution.
  • Integration Complexity
    Integrating SageMaker with other tools and systems within your workflow might require additional development effort. Custom integrations can be complex and could involve additional overhead to set up and maintain.

Saturn Cloud features and specs

  • Scalability
    Saturn Cloud allows users to scale their computational resources up or down easily, which is beneficial for handling varying workloads.
  • Managed Environment
    It provides a managed environment for data science projects, meaning users can focus more on their data analysis without worrying about infrastructure maintenance.
  • Collaborative Features
    Tools like Jupyter notebooks and dashboards can be shared among team members, fostering better collaboration.
  • Integration with Popular Tools
    Saturn Cloud integrates well with popular data science libraries and platforms such as Dask, PyTorch, and TensorFlow.
  • Cost-Effectiveness
    It often provides a more cost-effective solution compared to setting up and maintaining an on-premise infrastructure.

Possible disadvantages of Saturn Cloud

  • Learning Curve
    New users may face a learning curve to understand and utilize all the features effectively.
  • Dependency on Internet Connectivity
    Since it's a cloud-based service, access is heavily reliant on internet connectivity, which can be a limitation in areas with poor connection.
  • Pricing Complexity
    Understanding the pricing model can be challenging, as costs may vary based on usage and resource allocation.
  • Vendor Lock-in
    Using Saturn Cloud or any cloud platform can potentially lead to vendor lock-in, making it difficult to switch providers without significant cost or effort.

Amazon SageMaker videos

+ Add

Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks

More videos:

  • Review - An overview of Amazon SageMaker (November 2017)

Saturn Cloud videos

+ Add

Getting Started with Saturn Cloud

More videos:

  • Review - SATURN CLOUD || ECLIPSE || BLENDERS EYEWEAR || UNBOXING
  • Review - Saturn Cloud: Overview

Category Popularity

0-100% (relative to Amazon SageMaker and Saturn Cloud)

Amazon SageMaker

Saturn Cloud

Data Science And Machine Learning
77 77%
Data Science And Machine Learning
23% 23
Office & Productivity
57 57%
Office & Productivity
43% 43
AI
100 100%
AI
0% 0
Development
0 0%
Development
100% 100

User comments

Share your experience with using Amazon SageMaker and Saturn Cloud. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon SageMaker and Saturn Cloud

Amazon SageMaker Reviews

7 best Colab alternatives in 2023
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning. It allows users to write code, track experiments, visualize data, and perform debugging and monitoring all within a single, integrated visual interface, making the process of developing, testing, and deploying models much more manageable.
Source: deepnote.com

Saturn Cloud Reviews

  1. Filip
    One of the best cloud based solutions for data science projects

    Smooth and bug free experience. There are ready data science images with pre loaded packages for most common scenarios, making you focus on the project/problem and leave the infrastructure part to Saturn Cloud.

    👍 Pros:    Easy jupyter setup with boot scripts|Dask support|Easy to spin cluster for model training or grid search|Great and minimalistic ui
    👎 Cons:    Access to cheaper spot instances needed
  2. Anh Q Nguyen
    · Student at University ·
    Amazing computes

    True story, way better than just sweating Colab. The best and cheapest compute services there is.

    👍 Pros:    Cheap price|Easy to use|Can use terminal
  3. abhijit
    · student at - ·
    An amazing cloud computing platform

    I have started using this to run the computations which generally require like 64+GB of RAM, and the procedure to setup the enviroment is also nice. Got all the R packages running smoothly.

    🏁 Competitors: Google Cloud Platform
The Best ML Notebooks And Infrastructure Tools For Data Scientists
Saturn Cloud hosts Jupyter Notebooks and has seamless management capabilities for Python environments on the cloud. You can start a project by creating a Jupyter notebook and selecting the disk space and your machine’s size. The configurations meet the requirements for most of the practical data science projects. Automatic version control, customisable environments, and a...
Source: analyticsindiamag.com

Social recommendations and mentions

Based on our record, Amazon SageMaker should be more popular than Saturn Cloud. It has been mentiond 44 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon SageMaker mentions (44)

  • Dashboard for Researchers & Geneticists: Functional Requirements [System Design]
    Leverage Amazon SageMaker: For machine learning (ML) tasks, users can leverage Amazon SageMaker to analyze large datasets and build predictive models. - Source: dev.to / 13 days ago
  • Address Common Machine Learning Challenges With Managed MLflow
    MLflow, an Apache 2.0-licensed open-source platform, addresses these issues by providing tools and APIs for tracking experiments, logging parameters, recording metrics and managing model versions. It also helps to address common machine learning challenges, including efficiently tracking, managing, deploying ML models and enhancing workflows across different ML tasks. Amazon SageMaker with MLflow offers secure... - Source: dev.to / about 1 month ago
  • How I suffered my first burnout as software developer
    Our first task for the client was to evaluate various MLOps solutions available on the market. Over the summer of 2022, we conducted small proofs-of-concept with platforms like Amazon SageMaker, Iguazio (the developer of MLRun), and Valohai. However, because we weren’t collaborating directly with the teams we were supposed to support, these proofs-of-concept were limited. Instead of using real datasets or models... - Source: dev.to / 4 months ago
  • 👋🏻Goodbye Power BI! 📊 In 2025 Build AI/ML Dashboards Entirely Within Python 🤖
    Taipy’s ecosystem doesn’t stop at dashboards. With Taipy you can orchestrate data workflows and create advanced user interfaces. Besides, the platform supports every stage of building enterprise-grade applications. Additionally, Taipy’s integration with leading platforms such as Databricks, Snowflake, IBM WatsonX, and Amazon SageMaker ensures compatibility with your existing data infrastructure. - Source: dev.to / 5 months ago
  • Understanding the MLOps Lifecycle
    Based on your technological stack, various services are used to deploy machine learning models. Some popular services are AWS Sagemaker, Azure Machine Learning, Vertex AI, and many others. - Source: dev.to / 5 months ago
View more

Saturn Cloud mentions (7)

  • How I suffered my first burnout as software developer
    After the MLOps tooling evaluation, our focus shifted to data engineering. Some teams in the company were already using tools like Dask and xarray to manage and process their datasets. The architect was determined to build a data lake for the organization. The vision was to make xarray datasets accessible via Intake, using a Dask-capable computing platform. For the compute platform, we explored services like... - Source: dev.to / 4 months ago
  • Where to run computationally intensive analyses?
    Not 100% sure of your intention, but if you work with python, and you're familiar with (or can spend the time learning) dask, and willing to pay, you can consider coiled.io or saturncloud.io that offer managed dask that you can scale and use GPUs etc (again, not sure if applicable to your use case). Source: about 2 years ago
  • free-for.dev
    SaturnCloud - Data science cloud environment, that allows to run Jupyter notebooks and Dask clusters. 30 hours free computation and 3 hours of Dask per month. - Source: dev.to / over 2 years ago
  • [P] Serverless Jupyter Labs with GPUs, CPUs and high-speed storage
    I think your site looks good and I have used the type of service you offer, but there are 2 potential problems. As SheepherderPatient51 said,Google already offers all of this for free (and so does https://kaggle.com and https://www.paperspace.com ).  There are also other sites just like yours such as https://deepnote.com,https://saturncloud.io, and https://lambdalabs.com . Source: over 2 years ago
  • Show HN: Free Hosted JupyerLab with GPU
    * How does it differ from other GPU cloud providers that offer ready to use Jupyter notebooks? (E.g. https://support.genesiscloud.com/support/solutions/articles/47001170102-running-jupyter-notebook-or-jupyterlab-on-your-instance or https://saturncloud.io/). - Source: Hacker News / about 3 years ago
View more

What are some alternatives?

When comparing Amazon SageMaker and Saturn Cloud, you can also consider the following products

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

Deepnote - A collaboration platform for data scientists

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics

Azure Machine Learning Studio - Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.

Apache Zeppelin - A web-based notebook that enables interactive data analytics.

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