Amazon Personalize and Amazon Forecast at ClearScale
ClearScale helps customers get more from AI- and ML-powered apps by integrating services that allow them to do more. Amazon Personalize and Amazon Forecast are two services that are most helpful and sought after by customers across various industries.
To develop apps that deliver real-time personalized recommendations, Amazon Personalize is the go-to service for ClearScale and its customers.
Amazon Personalize is an API-based tool that automates and accelerates the complex machine learning tasks integral to building, training, refining and deploying personalization models. Its key benefits are the following:
- Automates and accelerates the complex machine learning tasks integral to building, training, refining, and deploying personalization models.
- Leverages the same machine learning modeling that Amazon uses in its retail business to drive personalization.
- Trains, tunes, and deploys custom machine learning models, while provisioning the necessary infrastructure and managing the full machine learning pipeline.
- Provides our customers with results on a pay-as-you-use basis. Customers pay only for what they use, and there are no minimum fees and no upfront commitments.
- Keeps customer data private at all times.
Amazon Forecast is ClearScale’s top choice in developing apps that deliver highly accurate forecasts.
Amazon Forecast is a fully managed service that uses machine learning to combine historical time series data with other variables to build forecasts for just about any industry and use case. Specifically, it:
- Achieves forecasting accuracy levels that used to take months of engineering in as little as a few hours.
- Helps generate forecasts that are up to 50% more accurate and forecasts events at scale.
- Incorporates very large volumes of historical data, ensuring it doesn’t miss out on important signals from the past that are otherwise lost in the noise.
- Considers related but independent data, which can offer an important context.
- Leverages relationships between variables to quickly recognize complex patterns and improve forecast accuracy, setting up a data pipeline, ingesting data, training a model, providing accuracy metrics, and performing forecasts.
- Creates models unique to customers’ data, which means the predictions are custom fit to their businesses.
- Provides customers with results on a pay-as-you-use basis. Customers pay only for what they use, and there are no minimum fees and no upfront commitments.
- Protects every interaction with encryption and ensures that sensitive information is kept secure and confidential.
Our Amazon Personalize and Amazon Forecast Use Cases
Here are just some of the ways ClearScale employs Amazon Personalize and Forecast.
We integrate Amazon Personalize it into apps to:
- Produce models that can accurately forecast demand for products, supplies, content and other information at highly individualized levels. The forecasts can be exported in batch in CSV format and imported back into management systems to determine inventory requirements.
- Forecast key financial metrics such as revenue, expenses, and cash flow across multiple time periods and monetary units; assess the expected accuracy of the forecast; and determine if more data is required before using the model in production.
- Provide visualization of forecasts to help customers make informed decisions.
- Plan for the right level of available resources to maximize revenue and control costs.
We use Amazon Forecast to:
- Enable apps and websites to tailor content and recommendations to users’ individual behaviors, history, and preferences.
- Improve site search results for individual users by reranking search results using the behavioral data from that particular user’s past application interactions or behaviors.
- Help customers deliver the most relevant communications or other information to specific individuals.
ClearScale Puts Amazon Personalize and Amazon Forecast into Action
ClearScale helped a client develop a platform to enable colleges and universities to more finely tune their student recruitment and retention efforts for cost savings and better long-term student outcomes.
The idea was to gather a broad range of detailed data on prospective students and run it against machine learning algorithms to generate continuously refined, probability-based intelligence. The information could then be used to identify students most likely to prosper at the specific institutions.