Machine learning used to be a field dominated by a select group of data scientists, making it seemingly out of reach for most companies due to its associated costs and complexities. However, due to many managed and foundational AI improvements, I have embraced the transformative power of machine learning. Due to a recent client project, I want to highlight two incredible solutions introduced by Amazon. These managed services, Amazon Personalize and Amazon Forecast, have not only made deep machine learning accessible but also remarkably affordable.
Launched a couple years ago, Amazon Personalize and Amazon Forecast are plug-and-play ML services that provide real-time personalization and time series forecasting with minimal machine learning experience required. These services offer businesses complex customer insights, highly accurate forecasts, and an almost immediate return on investment. What sets them apart is their pay-as-you-go pricing model, eliminating minimum fees or upfront commitments for companies.
Amazon Personalize: Unlocking Personalized Experiences
Amazon Personalize serves as a tool for building sophisticated recommendation engines based on deep machine learning. Having been tested internally for years on Amazon.com, Personalize empowers us to create personalized customer experiences for products, content, and promotions without the need for extensive machine learning expertise. What’s remarkable is that with Amazon’s pay-for-use pricing, what used to cost $100k in investment can now be deployed for a fraction of the cost.
In essence, Personalize takes historical data (clicks, page views, signups, purchases) and an inventory of items to be recommended. It then processes this data to create and train an accurate personalization model for your specific data. This model becomes a “campaign,” optimized for serving recommendations to your customers through a simple API call.
I have leveraged Personalize for various use cases that provide rapid ROI for my clients. For instance, in the realm of e-commerce, Personalize has proven to be a game-changer. For a real estate client, I enhanced their “recommended homes” section using Personalize, moving beyond mere traffic-based recommendations. By delving into a broader set of variables with deep machine learning, we now serve sophisticated, targeted recommendations that expedite the decision-making process and lead to higher customer satisfaction.
Additionally, Personalize finds application in IoT device management, where it analyzes vast data from smart home devices to make recommendations for optimizing tasks. Moreover, in B2B environments, it aids in internal resource planning, determining the best-suited developer for a project based on past performance and specific attributes.
Amazon Forecast: Revolutionizing Forecasting with Machine Learning
In my opinion, Amazon Forecast holds immense potential to transform how companies harness machine learning. As a fully managed service, it can improve forecasting by 50% and accurately recognize complex and irregular trends.
Forecast integrates historical time series data with related variables to build forecasts. According to AWS, it can predict a third of the future based on the past. The more data available, the farther into the future you can predict. Much like Personalize, Forecast examines, analyzes, and processes data to create machine learning models for forecasting.
I recommend Forecast to clients interested in predicting product supply and demand, resource planning, financial forecasting, and seasonal trend analysis. AWS is continually enhancing the service to handle more complex “what if” scenarios for forecasting, providing invaluable insights to our clients.
As an example of Forecast’s prowess in analyzing complex data, I’ve worked with a client monitoring 10 KPIs daily, each with 30 variables. Using Forecast, they can rely on deep machine learning to uncover correlations and produce an accurate picture of future conditions.
With over three years of data to load into Forecast, we estimate that with 10 KPIs and 300 variables, it will cost our client only $6 per year to forecast their KPIs every day—highlighting the value of AWS volume pricing.
Best Practices for Implementation
Whether using Personalize or Forecast, the implementation process remains consistent:
- Identify your KPIs: Begin by identifying Key Performance Indicators to measure success.
- Organize your historical data: Organize data before feeding it into the ML model.
- Validate your model: Test different use cases to determine model accuracy.
- Monitor results: Regularly monitor changes and results, making adjustments as needed.
Both Amazon Personalize and Amazon Forecast offer powerful, cost-effective opportunities to leverage machine learning for application improvement. As Amazon continues to innovate its ML solutions, we see significant strategic advantages for companies ready to explore these transformative tools.