Build ML models with advanced configurations, gain Model Leaderboard visibility on SageMaker Canvas

/ Tutorial

Introduction

In today’s data-driven world, machine learning (ML) has become an essential tool for businesses to generate accurate predictions and gain valuable insights. However, building ML models often requires technical expertise and coding skills, making it challenging for non-technical stakeholders to leverage this powerful technology.

To address this issue, Amazon SageMaker Canvas provides a no-code workspace that enables analysts and citizen data scientists to build ML models without writing a single line of code. With convenient and intuitive features, users can now customize advanced model building configurations, gain Model Leaderboard visibility, and optimize machine learning algorithms to achieve better results. In this guide, we will explore the various capabilities of SageMaker Canvas and provide insights into how it can be effectively utilized.

Overview of SageMaker Canvas

SageMaker Canvas is an integrated development environment (IDE) that simplifies the process of building and deploying ML models. It offers a visual interface with drag-and-drop functionality, allowing users to create ML workflows effortlessly. With Canvas, analysts and citizen data scientists can focus on exploring data, selecting algorithms, and optimizing models instead of getting tangled in the intricacies of coding.

Features

  1. No-code model building: SageMaker Canvas eliminates the need for writing complex code while building ML models. Users can simply drag and drop components to construct the desired ML workflow.

  2. Advanced model configurations: With the latest update, SageMaker Canvas now supports advanced configurations that provide greater control over the model building process. These configurations include selecting training methods (ensemble or hyperparameter optimization) and choosing specific algorithms.

  3. Customizable data split ratio: Users can customize the ratio of training and validation data during the model building process. This allows for better training and evaluation of ML models.

  4. Limits on autoML iterations and job run time: To ensure efficient and optimized model building, SageMaker Canvas allows users to set limits on autoML iterations and job run time. This feature enables users to control the duration and complexity of the model building process.

  5. Model Leaderboard visibility: SageMaker Canvas provides a Model Leaderboard that displays the performance metrics of each trained model. This allows users to compare and select the best-performing models for further analysis and deployment.

Benefits

  1. Democratizing ML: By providing a no-code environment, SageMaker Canvas democratizes ML by allowing non-technical stakeholders to leverage the power of ML models. This enables businesses to extract valuable insights from their data without the need for technical expertise.

  2. Customization without coding: With advanced configurations, SageMaker Canvas empowers citizen data scientists to experiment with different ML algorithms and techniques. This customization enables them to tailor the model development process to suit their specific business requirements.

  3. Optimization and performance: The ability to customize training methods, algorithms, and data split ratios allows users to optimize models for better performance. This helps in achieving more accurate predictions and improves the quality of insights gained from ML models.

Getting Started with SageMaker Canvas

Before diving into the advanced configurations and features of SageMaker Canvas, it is important to understand the core concepts and functionality. In this section, we will provide a step-by-step guide on how to get started with SageMaker Canvas.

Step 1: Data ingestion and exploration

To begin building ML models with SageMaker Canvas, the first step is to ingest and explore the data. SageMaker Canvas provides seamless integration with various data sources, allowing users to import datasets in different formats such as CSV, JSON, or Parquet. Once the data is imported, users can easily visualize and explore it using interactive charts and graphs.

Step 2: Model workflow creation

After exploring the data, the next step is to create a model workflow. SageMaker Canvas offers a drag-and-drop interface, enabling users to add components to the canvas and connect them to construct the desired workflow. The components can include data preprocessors, feature transformers, model trainers, and evaluators.

Step 3: Model training and evaluation

Once the model workflow is constructed, users can initiate the training process. SageMaker Canvas enables users to select the training method, such as ensemble or hyperparameter optimization, and choose specific ML algorithms. Additionally, the data split ratio can be customized to allocate the desired proportion of data for training and validation.

After the model is trained, SageMaker Canvas automatically evaluates its performance and displays the metrics on the Model Leaderboard. Users can compare the performance of different models and select the most suitable one for further analysis and deployment.

Step 4: Deployment and inference

After selecting the best-performing model, it can be deployed for inference. SageMaker Canvas allows users to deploy models on various platforms, including AWS Elastic Inference and Amazon EC2 instances. Once deployed, the models can generate predictions on new data, enabling businesses to make data-driven decisions in real-time.

Advanced Model Configurations

SageMaker Canvas offers advanced model configurations that allow users to tailor the model building process according to their specific requirements. These configurations provide greater control and flexibility, enabling users to optimize models for better performance. Let’s explore some of these advanced model configurations.

Selecting training methods

SageMaker Canvas supports two training methods: ensemble and hyperparameter optimization. Ensemble methods combine multiple ML models to improve prediction accuracy. Hyperparameter optimization automatically searches for the best combination of hyperparameters for a given ML algorithm. Users can experiment with both methods to determine which one works best for their data and problem domain.

Choosing specific algorithms

SageMaker Canvas offers a wide range of ML algorithms that users can choose from. These algorithms include linear regression, decision trees, random forests, neural networks, and more. Users can select the most appropriate algorithm based on the nature of their data and the prediction task at hand. Experimenting with different algorithms can help users gain insights into which methods work best for their data and improve the model’s quality and performance.

Customizing data split ratio

During the model training process, it is crucial to split the data into training and validation sets. SageMaker Canvas allows users to customize the ratio of the training and validation data. This customization ensures that an optimal proportion of data is used for training and that the model’s performance is effectively evaluated on the validation set.

Limits on autoML iterations and job run time

To avoid long and resource-intensive model training processes, SageMaker Canvas enables users to set limits on autoML iterations and job run time. AutoML iterations define the number of iterations the model building process will go through to find the best-performing model. Job run time limits allow users to control the duration and complexity of the model building process. Setting these limits helps in optimizing the model development process and ensuring timely results.

Model Leaderboard Visibility

The Model Leaderboard in SageMaker Canvas provides a comprehensive view of the trained models’ performance metrics. This feature allows users to compare different models and select the best-performing one for further analysis and deployment. Let’s explore the key aspects and benefits of Model Leaderboard visibility in more detail.

Performance metrics visualization

The Model Leaderboard displays various performance metrics for each trained model. These metrics can include accuracy, precision, recall, F1 score, and more, depending on the nature of the prediction task. Visualizing these metrics side by side allows users to compare models’ performance and identify the most accurate and reliable models.

Iterative model improvement

By comparing different models on the Model Leaderboard, users can observe the incremental improvements in performance metrics. This iterative approach enables users to refine and optimize their ML models over time. The ability to track and measure the impact of various model building configurations helps users understand which settings work best for their data and improve the overall quality and accuracy of predictions.

Enhanced model selection process

The Model Leaderboard simplifies the model selection process by providing a clear overview of each model’s performance. Users can quickly identify the models that meet their specific criteria and select them for further analysis and deployment. The ability to choose the best-performing models based on data-driven insights ensures that businesses make informed decisions and generate accurate predictions.

Collaboration and knowledge sharing

The Model Leaderboard promotes collaboration and knowledge sharing among data scientists and stakeholders. By sharing the Model Leaderboard with others, users can gain valuable feedback and insights from their colleagues. This collaborative approach enhances the model development process and fosters a culture of continuous improvement within the organization.

Conclusion

In conclusion, Amazon SageMaker Canvas is a powerful tool that democratizes ML by providing a no-code workspace for building accurate ML models. With advanced model configurations, users can now customize their ML workflows and optimize models for better performance. The Model Leaderboard visibility enables users to compare and select the best-performing models, ensuring accurate predictions and valuable insights. By leveraging the capabilities of SageMaker Canvas, businesses can unlock the full potential of machine learning and drive innovation in various domains.