This chapter covers
- Review of the topics in this book
- How two companies using machine learning improved their business
- Case study 1: Implementing a single machine learning project in your company
- Case study 2: Implementing machine learning at the heart of everything your company does
Throughout the book, you have used AWS SageMaker to build solutions to common business problems. The solutions have covered a broad range of scenarios and approaches:
- Using XGBoost supervised learning to solve an approval routing challenge
- Reformatting data so that you could use XGBoost again, but this time to predict customer churn
- Using BlazingText and Natural Language Processing (NLP) to identify whether a tweet should be escalated to your support team
- Using unsupervised Random Cut Forest to decide whether to query a supplier’s invoice
- Using DeepAR to predict power consumption based on historical trends
- Adding datasets such as weather forecasts and scheduled holidays to improve DeepAR’s predictions
In the previous chapter, you learned how to serve your predictions and decisions over the web using AWS’s serverless technology. Now, we’ll wrap it all up with a look at how two different companies are implementing machine learning in their business.