Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how well the model fits to the data and what columns are driving the prediction, and allows business analysts to run experiments with varied datasets so they can select the best-performing model.
Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own—without requiring any ML experience or having to write a single line of code.
In this post, we showcase how to to train a time series forecasting model faster with quick build training in Canvas.
Until today, training a time series forecasting model took up to 4 hours via the standard build method. Although that approach has the benefit of prioritizing accuracy over training time, this was leading frequently to long training times, which in turn wasn’t allowing for fast experimentation that business analysts across all sorts of organizations usually seek. Starting today, Canvas allows you to employ the Quick build feature for training a time series forecasting model, adding to the use cases for which it was already available (binary and multi-class classification and numerical regression). Now you can train a model and get explainability information in under 20 minutes, with everything in place to start generating inference.
To use the Quick build feature for time series forecasting ML use cases, all you need to do is upload your dataset to Canvas, configure the training parameters (such as target column), and then choose Quick build instead of Standard build (which was the only available option for this type of ML use case before today). Note that quick build is only available for datasets with fewer than 50,000 rows.
Let’s walk through a scenario of applying the Quick build feature to a real-world ML use case involving time series data and getting actionable results.
Anyone who has worked with ML, even if they possess no relevant experience or expertise, knows that the end result is only as good as the training dataset. No matter how much of a good fit the algorithm is that you used to train the model, the end result will reflect the quality of the inferencing on unseen data, and won’t be satisfactory if the training data isn’t indicative of the given use case, is biased, or has frequent missing values.
For the purposes of this post , we use a sample synthetic dataset that contains demand and pricing information for various items at a given time period, specified with a timestamp (a date field in the CSV file). The dataset is available on GitHub. The following screenshot shows the first ten rows.
Solving a business problem using no-code ML with Canvas is a four-step process: import the dataset, build the ML model, check its performance, and then use the model to generate predictions (also known as inference in ML terminology). If you’re new to Canvas, a prompt walking you through the process appears. Feel free to spend a couple of minutes with the in-app tutorial if you want, otherwise you can choose Skip for now. There’s also a dedicated Getting Started guide you can follow to immerse yourself fully in the service if you want a more detailed introduction.
We start by uploading the dataset. Complete the following steps:
Canvas launches an in-memory AutoML process that trains multiple time series forecasting models with different hyperparameters. In less than 20 minutes (depending on the dataset), Canvas will output the best model performance in the form of five metrics.
Let’s dive deep into the advanced metrics for time series forecasts in Canvas, and how we can make sense of them:
For more information about advanced metrics, refer to Use advanced metrics in your analyses.
Built-in explainability is part of the value proposition of Canvas, because it provides information about column impact on the Analyze tab. In this use case, we can see that price has a great impact on the value of demand. This makes sense because a very low price would increase demand by a large margin.
After we’ve analyzed the performance of our model, we can use it to generate predictions and test what-if scenarios.
The following screenshot shows the forecast for item_002.
We can expect an increase in demand in the coming months. Canvas also provides a probabilistic threshold around the expected forecast, so we can decide whether to take the upper bound of the prediction (with the risk of over-allocation) or the lower bound (risking under-allocation). Use these values with caution, and apply your domain knowledge to determine the best prediction for your business.
Canvas also support what-if scenarios, which makes it possible to see how changing values in the dataset can affect the overall forecast for a single item, directly on the forecast plot. For the purposes of this post, we simulate a 2-month campaign where we introduce a 50% discount, cutting the price from $120 to $60.
This is a lower price than the initial $100–120, therefore we expect a sharp increase in product demand. This is confirmed by the forecast, as shown in the following screenshot.
To avoid incurring future session charges, log out of SageMaker Canvas.
In this post, we walked you through the Quick build feature for time series forecasting models and the updated metrics analysis view. Both are available as of today in all Regions where Canvas is available. For more information, refer to Build a model and Use advanced metrics in your analyses.
To learn more about Canvas, refer to these links:
To learn more about other use cases that you can solve with Canvas, check out the following posts:
Start experimenting with Canvas today, and build your time series forecasting models in under 20 minutes, using the 2-month Free Tier that Canvas offers.
Davide Gallitelli is a Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customers throughout Benelux. He has been a developer since he was very young, starting to code at the age of 7. He started learning AI/ML at university, and has fallen in love with it since then.
Nikiforos Botis is a Solutions Architect at AWS, looking after the public sector of Greece and Cyprus, and is a member of the AWS AI/ML technical community. He enjoys working with customers on architecting their applications in a resilient, scalable, secure, and cost-optimized way.