One of the biggest benefits of cloud computing is being able to take advantage of artificial intelligence and machine learning at scale. By abstracting away IT infrastructure, cloud service providers (CSPs), like AWS, free up IT teams to focus more on the fun and challenging stuff – like building powerful AI/ML algorithms that improve over time. AWS, especially, has invested heavily in its AI/ML offerings. This opens up countless opportunities for organizations to create value from big data.

However, it can be overwhelming to jump into AI/ML for a variety of reasons. Some organizations are still dipping their feet into the cloud and have not yet pursued more advanced use cases. Others have tried using ML models in production but have failed to see any positive return on investment. Companies have also struggled with model degradation.

Back in 2018, Gartner reported that 85% of ML projects were failing, meaning model outputs were tainted by bias or teams were struggling to maintain model quality. As a result, some leaders have turned away from AI/ML technology for now.

The good news is that we’ve learned much more about how to preserve ML models in production. And CSPs like AWS have studied the challenges that data science and AI/ML teams face in the real world. This has empowered them to improve their offerings significantly and increase the likelihood of AI/ML success for users.

The Amazon Persanlize Solution

One such solution that is growing in popularity is Amazon Personalize. This service helps developers create and deploy recommendation engines quickly at scale. Through the power of machine learning, Amazon Personalize can take in massive volumes of data, do some modeling, and then provide tailored recommendations for virtually any domain.

A typical Amazon Personalize workflow looks like this:

  • Data (user events, conversions, page views, etc.) is loaded into Amazon Personalize
  • Engineers choose the ML algorithm and use case they want to apply to their data
  • The model then goes through fine-tuning based on business requirements
  • Model recommendations are published to an API or visualization interface

With Amazon Personalize, it’s easy to optimize recommendations, segment users, and emphasize specific organizational priorities. Companies everywhere are using the solution to push tailored suggestions on websites, applications, newsletters, streaming platforms, and more.

The key with Amazon Personalize is knowing how to integrate it into a broader sophisticated data science practice on the cloud that promotes long-term AI success. Amazon Personalize can’t fix a broken data management foundation. If bad data goes in, bad recommendations come out. That’s why working with an AWS consultancy, like ClearScale, can make sense to ensure data readiness for AI.

How Do ClearScale and AWS Support AL/ML-powered Personalization?

ClearScale is an AWS Premier Tier Services Partner with 11 AWS competencies, including the Machine Learning competency. This demonstrates our deep knowledge of the AWS platform and ability to help businesses solve their biggest IT challenges on the cloud. We love enabling IT leaders to leverage AI/ML technology to build better products and services.

We’ve used Amazon Personalize in a number of engagements that have generated tangible results for our clients. For instance, we worked with an online flower retailer that wanted to show personalized product recommendations on its digital storefront. We built a solution based on Amazon Personalize that uses a number of data points, including purchase history, cart additions, and the amount of time spent browsing certain items, to determine what other products could be interesting to shoppers. The recommendation engine continues to learn over time and requires minimal oversight from in-house data science engineers.

In the media and entertainment space, we helped another client use Amazon Personalize to build a movie recommendation proof of concept. We used AWS Step Functions and weekly Lambda triggers to pull in new data that would then flow into Amazon Personalize. As a result of our work, the client’s subscriber base now receives high-quality content recommendations based on what they’ve viewed previously.

Amazon Personalize was also instrumental in enabling PBS to build a recommendation engine that served millions of users. This project showcased how Amazon Personalize can scale with massive demand and integrate seamlessly into a broader MLOps function. In addition to implementing the ML-based recommendation engine, we also built a data ingestion pipeline capable of enriching data for Amazon Personalize.

How Can Amazon Personalize Benefit You?

With these examples in mind, think about how Amazon Personalize could elevate your products and services:

  • How can you tailor your products more to individual users?
  • How can you increase conversions or customer satisfaction through personalization?
  • What are your data science and ML engineers doing that could be automated?
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