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- Amazon SageMaker Canvas Features
Amazon SageMaker Canvas features
Build highly accurate ML models using a visual interface, no code required
Chat-driven ML development with Amazon Q Developer
Translate business problems into ML workflows
Amazon Q Developer helps to bridge the gap between business challenges and ML models. It expertly translates business problems into step-by-step ML workflows and explains ML terms using non-technical language.
Build ML models using a guided workflow
Amazon Q Developer expertly guides users at every step of model development, from preparing data to building, training, and deploying ML models. Using a chat interface, Amazon Q Developer provides contextual assistance and helps users navigate the end-to-end ML workflow to build production-ready ML models.
Data science best practices
Amazon Q Developer’s deterministic pipeline builder and advanced AutoML techniques support reproducibility and accuracy in model creation. By empowering users with advanced data science capabilities, Q Developer enables rapid experimentation while maintaining trust in model utility.
Transparency into the ML workflow
Amazon Q Developer maintains artifacts such as original and transformed datasets, as well as the data preparation pipelines created using natural language. In addition, models built using Q Developer can be registered into the SageMaker Model Registry, and model notebooks can be exported for further customization and integration.
Prepare Data
Data Sources
Data visualizations
No-code data transformation
Data Pipelines
Access and Build ML Models
Custom ML Models
Ready-to-use tabular, CV, and NLP models
SageMaker Canvas provides access to ready to use tabular, NLP, and CV models for use cases including sentiment analysis, object detection in images, text detection in images, and entities extraction. The ready-to-use models do not require model building, and are powered by AWS AI services, including Amazon Rekognition, Amazon Textract, and Amazon Comprehend.
Model Evaluation
After you’ve built your model, you can evaluate how well your model performs before deploying it to production using company data. You can easily compare model responses and select the best response for your needs.
Foundation Models
SageMaker Canvas provides access to ready-to-use foundation model (FMs) for content generation, text extraction, and text summarization. You can access FMs such as Claude 2, Llama-2, Amazon Titan, Jurassic-2, and Command (powered by Amazon Bedrock) as well as publicly available FMs such as Falcon, Flan-T5, Mistral, Dolly, and MPT (powered by SageMaker JumpStart) and tune them using your own data.
Generate ML Predictions
Interactive what-if analysis and batch predictions
SageMaker Canvas offers visual what-if-analysis so you can change model inputs and then understand how the changes impact individual predictions. You can create automated batch predictions for an entire dataset, and, when the dataset is updated, you ML model is automatically updated. After the ML model is updated, you can review the updated predictions from the SageMaker Canvas no-code interface.
Support for real-time predictions
Amazon QuickSight integration
Share model predictions with Amazon QuickSight to build dashboards that combine traditional business intelligence and predictive data in the same interactive visual. In addition, SageMaker Canvas models can be shared and integrated directly in QuickSight, allowing analysts to generate highly accurate predictions for new data within a QuickSight dashboard.
Leverage MLOps
SageMaker Model Registry integration
You can register ML models created in SageMaker Canvas to the SageMaker Model Registry with a single click in order to integrate the model into existing model deployment CI/CD processes.
Model sharing with SageMaker Studio
You can share your SageMaker Canvas models with data scientists who use SageMaker Studio. Then data scientists can review, update, and share updated models with you or deploy your model for inference.