What is Amazon Sagemaker Canvas?
With Amazon SageMaker Canvas, you can create, test, and implement production-ready machine learning (ML) models without knowing any code and transform data at the petabyte scale. It simplifies the entire machine learning lifecycle in a safe and cohesive business setting. Now that SageMaker Canvas has Amazon Q Developer, you can use conversational chat to obtain help with every step of your machine learning journey, from data preparation to model deployment.
Through model versioning and access restrictions, SageMaker Canvas guarantees governance, promotes team collaboration, and offers insight into the code that is developed. By democratizing machine learning development for people of all skill levels and without coding knowledge, SageMaker Canvas helps you solve business challenges more rapidly and spur innovation.
The obstacles of training business analysts, marketing analysts, data analysts, and data engineers machine learning are evident as a data scientist. AWS announced today that Amazon Q Developer is now available in Amazon SageMaker Canvas.
Through natural language interactions, Amazon Q Developer assists domain experts even those without ML expertise in creating precise, production-quality machine learning models. These users are guided by Amazon Q Developer, which dissects their business issues and examines their data to provide detailed instructions for creating unique machine learning models. It gives consumers control and visibility into each stage of the guided ML workflow, transforms their data to eliminate anomalies, and creates and assesses bespoke ML models to suggest the optimal one. With a shorter time to market, this enables businesses to develop more quickly. Additionally, it lessens their dependency on machine learning professionals, freeing them up to concentrate on more difficult technical problems.
When a marketing analyst says, for instance, “I want to predict home sales prices using home characteristics and past sales data,” Amazon Q Developer will convert this into a series of machine learning procedures that include examining pertinent customer data, creating many models, and suggesting the most effective strategy.
Amazon Q Developer then describes the issue and suggests the best kind of machine learning model. It also lists the prerequisites for the solution, such as the required dataset properties. After that, Amazon Q Developer asks if you want to select a target column or upload your dataset.
The dataset requirements, which include pertinent housing information, current home prices, and the regression model’s goal variable, are listed by Amazon Q Developer in the following phase. After that, it suggested the following actions: I would want to submit my dataset, Choose an already-existing dataset, I want to select a target column or create a new dataset. I’ll use the canvas-sample-housing.csv sample dataset as my current dataset for this demonstration.
Once the dataset has been chosen and loaded, Amazon Q Developer examines it and recommends median_house_value as the regression model’s target column. You agree by choosing to forecast the “median_house_value” column. Amazon Q Developer then goes on to specify the dataset features (such “location,” “housing_median_age,” and “total_rooms”) it will utilize in order to forecast the median_house_value.
You can inquire about the quality of the data before beginning model training because it cannot create a trustworthy model without high-quality data. For your complete dataset, Amazon Q Developer provides insightful responses.
To have a better understanding of the quality of the data, you can pose targeted queries regarding various attributes and their distributions.
It was surprised to learn from the preceding question that there is a significant range of extreme numbers in the “households” column, which may have an impact on the model’s predicted accuracy. So request that Amazon Q Developer address this exceptional issue.
If you would like to know the procedures that Amazon Q Developer took to make this change once the transformation is complete. In order to obtain the final, ready dataset for model training, Amazon Q Developer uses SageMaker Canvas data preparation capabilities to apply sophisticated data preparation procedures behind the scenes. You can examine and see the phases so that can visualize and duplicate the process.
Upon going over the data preparation procedures, choose to start your training job.
You can observe the training job’s progress in the conversation and the generated datasets once it has been launched.
Comprehensive metrics like the root mean square error (RMSE) for regression models, the confusion matrix, and precision-recall scores for classification models. When assessing model performance and making data-driven decisions, these are essential components that always look for, and it’s encouraging to see them presented in a way that non-technical users can understand to foster trust and facilitate appropriate governance while preserving the level of detail required by technical teams.
By choosing the new model from your Models or the Amazon Q discussion menu, you can view these metrics:
- Overview: The column impact analysis is displayed on this tab. In this instance, the main factor affecting my model is median_income.
- Rating RMSE measurements are among the model accuracy insights available on this tab.
- Advanced measurements For a more thorough assessment of the model, this tab shows the comprehensive Metrics table, Residuals, and Error density.
You may proceed to the latter phases of the ML workflow after examining these metrics and confirming the model’s functionality:
- Predictions: To verify my model’s performance in the actual world, you can test it using the Predictions tab.
- Deployment: In order to make my model usable in production, you can establish an endpoint deployment.
This reduces the deployment process a step that typically calls for extensive DevOps expertise to a simple procedure that business analysts can confidently manage.
Things to know
ML is made accessible to all companies via Amazon Q Developer:
Empowering all skill levels with ML: SageMaker Canvas now offers Amazon Q Developer, which enables data professionals, marketing analysts, and business analysts without ML expertise to solve business problems via a guided ML workflow. Users may utilize natural language to solve business challenges from data analysis and model selection to deployment, decreasing reliance on machine learning specialists like data scientists and allowing businesses to innovate more quickly with shorter time to market.
Streamlining the ML workflow: Simplifying the ML process With SageMaker Canvas‘s integration of Amazon Q Developer, users can prepare data and create, evaluate, and implement machine learning models in a transparent, guided process. Advanced data preparation and AutoML features offered by Amazon Q Developer democratize machine learning by enabling non-ML specialists to create incredibly accurate ML models.
Providing full visibility into the ML workflow: By producing the underlying code and technical artifacts like data transformation stages, model exploitability, and accuracy measures, Amazon Q Developer offers complete transparency into the machine learning workflow. This promotes cooperation in a safe setting by enabling cross-functional teams, including ML specialists, to examine, validate, and update the models as necessary.
Availability: The Amazon Q Developer is now available in Amazon SageMaker Canvas preview release.
Sagemaker Canvas Pricing
Pricing: Both Amazon Q Developer Pro Tier and Amazon Q Developer Free Tier users can now access Amazon Q Developer in SageMaker Canvas for free. However, resources like SageMaker Canvas workspace instances and other resources required for model generation or deployment are subject to regular fees. See the Amazon SageMaker Canvas Pricing page for further pricing details.
