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AWS Sagemaker vs Databricks

A comparison of AWS Sage Maker and Databricks.  Both satisify different use cases.  A key aspect is the principle of ‘cloud native’, meaning that if on AWS Cloud and this is a core principle, you would first try to use AWS Sagemaker (depending on the use cases).  Databricks can also be deployed as a platform on AWS.

10 Amazon SageMaker Project Ideas and Examples for Practice

AWS SageMaker 

Primary Purpose: Machine Learning Development and Deployment

SageMaker is a fully managed service by AWS designed for building, training, and deploying machine learning models. It provides end-to-end capabilities for the entire machine learning lifecycle.

Key Features:

  1. Model Training: SageMaker supports model training using various algorithms, including built-in algorithms, custom algorithms, and frameworks like TensorFlow and PyTorch.
  2. Model Deployment: After training, SageMaker facilitates deploying and hosting machine learning models in a scalable and serverless environment.
  3. AutoML (Auto Machine Learning): SageMaker AutoML provides automated machine learning capabilities for model selection and hyperparameter tuning.


  1. AWS Ecosystem Integration: SageMaker integrates seamlessly with other AWS services, allowing easy data access, storage in S3, and integration with services like AWS Glue and AWS Lambda.

Ease of Use:

  1. Managed Environment: SageMaker provides a managed environment, abstracting away infrastructure management, making it easier for data scientists and developers to focus on building and deploying models.

Use Cases:

  1. Predictive Analytics: SageMaker is suitable for applications that require predictive analytics, recommendation systems, image recognition, natural language processing, etc.
  2. Real-time Inference: Ideal for scenarios where real-time inference is needed, such as in web applications or APIs.

Pricing Model:

  1. Usage-Based Pricing: SageMaker pricing is based on the resources consumed during training and inference, offering flexibility based on actual usage.


Serverless Continuous Delivery with Databricks and AWS CodePipeline ...

Primary Purpose: Unified Analytics Platform

      • Databricks is a unified analytics platform designed for big data analytics and machine learning. It provides a collaborative environment for data engineers, data scientists, and analysts.

Key Features:

    • Unified Platform: Databricks unifies data engineering, data science, and business analytics in a collaborative workspace.
    • Apache Spark Integration: Databricks is built on Apache Spark, making it efficient for big data processing, analytics, and machine learning tasks.
    • Notebooks: Databricks notebooks allow users to create and share code, visualizations, and narrative text in an interactive environment.


  1. Integration with Apache Spark: Databricks is tightly integrated with Apache Spark, providing a distributed computing environment for large-scale data processing and analytics.
  2. Connectors: Databricks supports connectors to various data sources, allowing seamless integration with data lakes, databases, and other storage solutions.

Ease of Use:

  1. Collaboration: Databricks provides a collaborative platform where data engineers, data scientists, and analysts can work together in the same environment.
  2. Notebook Interface: Users can work with notebooks that support multiple programming languages like Python, Scala, R, and SQL.

Use Cases:

  1. Big Data Analytics: Databricks is ideal for big data processing and analytics, leveraging the power of Apache Spark for distributed computing.
  2. Machine Learning at Scale: Databricks supports scalable machine learning workflows using MLlib and integration with popular machine learning libraries.

Pricing Model:

  1. Subscription-Based Model: Databricks offers a subscription-based pricing model based on the features and resources used in the platform.

Key Differences:


    • SageMaker: Primarily focuses on machine learning model development, training, and deployment.
    • Databricks: Offers a unified analytics platform for big data processing, analytics, and collaborative work.


    • SageMaker: Primarily used by data scientists and developers building and deploying machine learning models.
    • Databricks: Used by a broader audience, including data engineers, data scientists, analysts, and business users.

Managed vs. Unified:

    • SageMaker: Provides a managed environment for machine learning workloads.
    • Databricks: Offers a unified analytics platform that includes data engineering, data science, and business analytics.

Programming Languages:

    • SageMaker: Supports machine learning frameworks like TensorFlow, PyTorch, etc.
    • Databricks: Supports multiple programming languages including Python, Scala, R, and SQL for various analytics tasks.


    • SageMaker: Integrates with other AWS services within the AWS ecosystem.
    • Databricks: Integrates with various data sources and storage solutions and is built on Apache Spark.


    • SageMaker: Focused on individual data scientists and developers.
    • Databricks: Emphasizes collaborative work with features like notebooks for code sharing and collaboration.

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