Transition your Amazon Forecast utilization to Amazon SageMaker Canvas

Amazon Forecast is a totally managed service that makes use of statistical and machine studying (ML) algorithms to ship extremely correct time sequence forecasts. Launched in August 2019, Forecast predates Amazon SageMaker Canvas, a well-liked low-code no-code AWS software for constructing, customizing, and deploying ML fashions, together with time sequence forecasting fashions.

With SageMaker Canvas, you get faster model building, cost-effective predictions, superior options akin to a mannequin leaderboard and algorithm choice, and enhanced transparency. It’s also possible to both use the SageMaker Canvas UI, which supplies a visible interface for constructing and deploying fashions while not having to jot down any code or have any ML experience, or use its automated machine studying (AutoML) APIs for programmatic interactions.

On this submit, we offer an outline of the advantages SageMaker Canvas presents and particulars on how Forecast customers can transition their use instances to SageMaker Canvas.

Advantages of SageMaker Canvas

Forecast prospects have been in search of larger transparency, decrease prices, quicker coaching, and enhanced controls for constructing time sequence ML fashions. In response to this suggestions, we now have made next-generation time sequence forecasting capabilities obtainable in SageMaker Canvas, which already presents a strong platform for making ready knowledge and constructing and deploying ML fashions. With the addition of forecasting, now you can entry end-to-end ML capabilities for a broad set of mannequin varieties—together with regression, multi-class classification, pc imaginative and prescient (CV), pure language processing (NLP), and generative synthetic intelligence (AI)—inside the unified user-friendly platform of SageMaker Canvas.

SageMaker Canvas presents as much as 50% quicker mannequin constructing efficiency and as much as 45% faster predictions on common for time sequence fashions compared to Forecast throughout varied benchmark datasets. Producing predictions is  considerably more cost effective than Forecast, as a result of prices are based mostly solely on the Amazon SageMaker compute resources used. SageMaker Canvas additionally supplies glorious mannequin transparency by providing direct entry to skilled fashions, which you’ll be able to deploy at your chosen location, together with quite a few mannequin perception reviews, together with entry to validation knowledge, model- and item-level efficiency metrics, and hyperparameters employed throughout coaching.

SageMaker Canvas contains the important thing capabilities present in Forecast, together with the power to coach an ensemble of forecasting fashions utilizing each statistical and neural community algorithms. It creates the most effective mannequin in your dataset by producing base fashions for every algorithm, evaluating their efficiency, after which combining the top-performing fashions into an ensemble. This method leverages the strengths of various fashions to supply extra correct and sturdy forecasts. You will have the pliability to pick out one or a number of algorithms for mannequin creation, together with the aptitude to guage the influence of mannequin options on prediction accuracy. SageMaker Canvas simplifies your knowledge preparation with automated options for filling in lacking values, making your forecasting efforts as seamless as attainable. It facilitates an out-of-the-box integration of exterior data, akin to country-specific holidays, by means of easy UI choices or API configurations. It’s also possible to benefit from its data flow function to attach with exterior knowledge suppliers’ APIs to import knowledge, akin to weather information. Moreover, you’ll be able to conduct what-if analyses straight within the SageMaker Canvas UI to discover how varied eventualities would possibly have an effect on your outcomes.

We’ll proceed to innovate and ship cutting-edge, industry-leading forecasting capabilities by means of SageMaker Canvas by reducing latency, decreasing coaching and prediction prices, and enhancing accuracy. This contains increasing the vary of forecasting algorithms we assist and incorporating new superior algorithms to additional improve the mannequin constructing and prediction expertise.

Transitioning from Forecast to SageMaker Canvas

At present, we’re releasing a transition bundle comprising two assets that can assist you transition your utilization from Forecast to SageMaker Canvas. The primary element features a workshop to get hands-on expertise with the SageMaker Canvas UI and APIs and to learn to transition your utilization from Forecast to SageMaker Canvas. We additionally present a Jupyter pocket book that exhibits remodel your current Forecast coaching datasets to the SageMaker Canvas format.

Earlier than we learn to construct forecast fashions in SageMaker Canvas utilizing your Forecast enter datasets, let’s perceive some key variations between Forecast and SageMaker Canvas:

• Dataset varieties – Forecast makes use of a number of datasets – goal time sequence, associated time sequence (optionally available), and merchandise metadata (optionally available). In distinction, SageMaker Canvas requires just one dataset, eliminating the necessity for managing a number of datasets.
  
• Mannequin invocation – SageMaker Canvas permits you to invoke the mannequin for a single dataset or a batch of datasets utilizing the UI in addition to the APIs. Not like Forecast, which requires you to first create a forecast after which question it, you merely use the UI or API to invoke the endpoint the place the mannequin is deployed to generate forecasts. The SageMaker Canvas UI additionally provides you the choice to deploy the mannequin for inference on SageMaker real-time endpoints. With just some clicks, you’ll be able to obtain an HTTPS endpoint that may be invoked from inside your utility to generate forecasts.

Within the following sections, we talk about the high-level steps for reworking your knowledge, constructing a mannequin, and deploying a mannequin utilizing SageMaker Canvas utilizing both the UI or APIs.

Construct and deploy a mannequin utilizing the SageMaker Canvas UI

We suggest reorganizing your knowledge sources to straight create a single dataset to be used with SageMaker Canvas. Discuss with Time Series Forecasts in Amazon SageMaker Canvas  for steerage on structuring your enter dataset to construct a forecasting mannequin in SageMaker Canvas. Nonetheless, in case you want to proceed utilizing a number of datasets as you do in Forecast, you will have the next choices to merge them right into a single dataset supported by SageMaker Canvas:

• SageMaker Canvas UI – Use the SageMaker Canvas UI to hitch the goal time sequence, associated time sequence, and merchandise metadata datasets into one dataset. The next screenshot exhibits an instance dataflow created in SageMaker Canvas to merge the three datasets into one SageMaker Canvas dataset.
  
• Python script – Use a Python script to merge the datasets. For pattern code and hands-on expertise in reworking a number of Forecast datasets into one dataset for SageMaker Canvas, discuss with this workshop.

When the dataset is prepared, use the SageMaker Canvas UI, obtainable on the SageMaker console, to load the dataset into the SageMaker Canvas utility, which makes use of AutoML to coach, construct, and deploy the mannequin for inference. The workshop exhibits merge your datasets and construct the forecasting mannequin.

After the mannequin is constructed, there are a number of methods to generate and devour forecasts:

• Make an in-app prediction – You possibly can generate forecasts utilizing the SageMaker Canvas UI and export them to Amazon QuickSight utilizing built-in integration or obtain the prediction file to your native desktop. It’s also possible to entry the generated predictions from the Amazon Simple Storage Service (Amazon S3) storage location the place SageMaker Canvas is configured to retailer mannequin artifacts, datasets, and different utility knowledge. Discuss with Configure your Amazon S3 storage to be taught extra in regards to the Amazon S3 storage location utilized by SageMaker Canvas.
• Deploy the mannequin to a SageMaker endpoint – You possibly can deploy the mannequin to SageMaker real-time endpoints straight from the SageMaker Canvas UI. These endpoints might be queried by builders of their purposes with a number of traces of code. You possibly can replace the code in your current utility to invoke the deployed mannequin. Discuss with the workshop for extra particulars.

Construct and deploy a mannequin utilizing the SageMaker Canvas (Autopilot) APIs

You should utilize the pattern code offered within the notebook in the GitHub repo to course of your datasets, together with goal time sequence knowledge, associated time sequence knowledge, and merchandise metadata, right into a single dataset wanted by SageMaker Canvas APIs.

Subsequent, use the SageMaker AutoML API for time series forecasting to course of the info, practice the ML mannequin, and deploy the mannequin programmatically. Discuss with the pattern notebook in the GitHub repo for an in depth implementation on practice a time sequence mannequin and produce predictions utilizing the mannequin.

Discuss with the workshop for extra hands-on expertise.

Conclusion

On this submit, we outlined steps to transition from Forecast and construct time sequence ML fashions in SageMaker Canvas, and offered an information transformation pocket book and prescriptive steerage by means of a workshop. After the transition, you’ll be able to profit from a extra accessible UI, cost-effectiveness, and better transparency of the underlying AutoML API in SageMaker Canvas, democratizing time sequence forecasting inside your group and saving time and assets on mannequin coaching and deployment.

SageMaker Canvas might be accessed from the SageMaker console. Time sequence forecasting with Canvas is obtainable in all areas the place SageMaker Canvas is obtainable. For extra details about AWS Area availability, see AWS Services by Region.

Sources

For extra data, see the next assets:

Concerning the Authors

Nirmal Kumar is Sr. Product Supervisor for the Amazon SageMaker service. Dedicated to broadening entry to AI/ML, he steers the event of no-code and low-code ML options. Outdoors work, he enjoys travelling and studying non-fiction.

Dan Sinnreich is a Sr. Product Supervisor for Amazon SageMaker, targeted on increasing no-code / low-code providers. He’s devoted to creating ML and generative AI extra accessible and making use of them to resolve difficult issues. Outdoors of labor, he might be discovered enjoying hockey, scuba diving, and studying science fiction.

Davide Gallitelli is a Specialist Options Architect for AI/ML within the EMEA area. He’s based mostly in Brussels and works carefully with buyer all through Benelux. He has been a developer since very younger, beginning to code on the age of seven. He began studying AI/ML in his later years of college, and has fallen in love with it since then.

Biswanath Hore is a Options Architect at Amazon Internet Companies. He works with prospects early of their AWS journey, serving to them undertake cloud options to handle their enterprise wants. He’s captivated with Machine Studying and, outdoors of labor, loves spending time together with his household.