Amazon Machine Learning vs Amazon SageMaker: What are the differences?

Key Differences Between Amazon Machine Learning and Amazon SageMaker

Amazon Machine Learning (Amazon ML) and Amazon SageMaker are both popular services offered by Amazon Web Services (AWS) that enable users to build and deploy machine learning models. Although they serve similar purposes, there are several key differences between the two platforms.

1. Ease of Use:

   • Amazon SageMaker provides a more comprehensive and flexible set of tools for building, training, and deploying machine learning models. It offers a graphical user interface (GUI) along with customizable Jupyter notebooks, making it suitable for both beginners and experienced data scientists.

   • On the other hand, Amazon ML is designed to be extremely user-friendly and requires minimal coding or machine learning expertise. It simplifies the machine learning process by providing wizards and templates, making it a more accessible choice for users with limited technical knowledge.

2. Model Complexity:

   • Amazon SageMaker supports a wide range of machine learning algorithms, including both traditional statistical methods and deep learning frameworks like TensorFlow and PyTorch. It allows users to create complex models by leveraging these algorithms and customizing them as needed.

   • In contrast, Amazon ML is more suitable for simple machine learning tasks. It primarily focuses on binary classification, multiclass classification, and regression problems, limiting the complexity of the models that can be built.

3. Data Preparation and Feature Engineering:

   • Amazon SageMaker provides extensive support for data preprocessing and feature engineering. It offers tools for data cleaning, transformation, and feature extraction, enabling users to prepare their data before training the models.

   • On the other hand, Amazon ML has limited data preparation capabilities. It can handle basic preprocessing tasks like handling missing values and one-hot encoding categorical variables, but it lacks the advanced feature engineering capabilities of SageMaker.

4. Deployment Options:

   • Amazon SageMaker offers a variety of deployment options, including real-time inference endpoints, batch inference, and automatic model hosting. It allows users to easily deploy their models at scale and integrate them into their applications or workflows.

   • In contrast, Amazon ML only supports real-time prediction endpoints, limiting the deployment options for the models created using this service.

5. Customization and Control:

   • Amazon SageMaker provides more flexibility and control over the machine learning workflow. It allows users to customize the training and inference code, choose different instance types, and configure parameters as per their requirements.

   • Conversely, Amazon ML abstracts much of the underlying complexity, providing limited customization options. It automates most of the machine learning process, making it suitable for users who prefer a more managed and less hands-on approach.

6. Pricing Model:

   • The pricing model for Amazon SageMaker is based on the resources used, such as training instances, storage, and inference endpoints. Users pay for the specific resources they consume, which provides cost flexibility based on their usage patterns.

   • Amazon ML, on the other hand, follows a more simplified pricing model based on prediction requests and training hours. This may be advantageous for users who have predictable or constrained usage patterns.

In summary, Amazon SageMaker offers more advanced features, customization options, and deployment flexibility compared to Amazon ML, making it suitable for complex machine learning tasks. However, Amazon ML provides a simpler and more user-friendly experience for users with limited technical expertise.