Onriva is an industry-transforming travel platform that is leveraging the latest standards and technologies (NDC), AI, and machine learning to provide a personalized one-stop shopping experience for all travel, including air, hotels, cars, packages, and more. To learn more, go to https://www.onriva.com/
Online travel services
Foster City, CA
Support rapid business growth by implementing machine learning to enable personalized travel itineraries that drive increased booking rates.
Machine learning (Amazon SageMaker), Amazon Simple Storage Service (Amazon S3), AWS Glue, AWS Lambda
Faster build of highly accurate training datasets.
Reduced costs and complexity, increased accuracy.
Rapid extract, transform, load (ETL) of data.
nClouds brought a deep expertise in designing and implementing big data infrastructure and helped (with) incorporating machine learning and AI for our recommender platform. Really appreciate their help at the very early stage of model development and getting our data ready for machine learning."
Chief Product Officer, Onriva
In a 2017 Accenture study, 67% of respondents said that they want brands to use previous travel information to help them make better travel decisions. So, it’s not surprising that companies in the travel industry have embraced machine learning to deliver new insights and provide customers with enhanced user experience.
By analyzing large datasets, machine learning-infused travel systems can predict travelers’ needs and provide them with personalized recommendations. As a result, the traveler benefits from getting relevant information at the right time, while the travel provider benefits from new customer acquisition, maximizing revenues, and increased customer engagement and loyalty.
Onriva wanted to apply machine learning to develop a powerful travel itinerary recommendation algorithm. To do so, they needed to enhance their AWS infrastructure to quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.
Onriva asked nClouds to help them build a machine learning model. The model would analyze user searches to make predictions on user patterns. The AWS machine learning model cycle is shown below:
nClouds began the project by creating Amazon SageMaker notebook instances. These are fully-managed, machine learning EC2 compute instances running the Jupyter Notebook App, an open-source web application to create and share documents that contain live code, equations, visualizations, and narrative text. Jupyter notebook use cases include machine learning, data cleaning and transformation, numerical simulation, statistical modeling, and data visualization.
Here is an illustration of the steps to using Amazon SageMaker to build, train, and deploy machine learning models at scale:
Onriva turned to nClouds, a Premier Consulting Partner in the Amazon Web Services Partner Network (APN), to partner with them in the build phase of implementing machine learning. nClouds applied their AWS technical expertise to create a dataset ready to support machine learning.
Onriva engaged with nClouds to help them with the build phase of implementing machine learning.
Users’ search results are provisioned by AWS Lambda before being incorporated in a transactional database. Onriva’s transactional database uses Amazon RDS, and their user interaction database uses Amazon EC2. Search results in JSON format are stored in Amazon S3.
AWS Glue ETL service prepares and loads Onriva’s data for analytics. That data is then applied to metamodels designed for fast machine learning and Apache Spark for data processing. The resulting analysis data is sent to Jupyter notebooks for training data exploration and preprocessing as well as to Amazon Athena for queries. And, as mentioned earlier, Amazon SageMaker performs predictive analysis and generates personalized travel itinerary recommendations for their users.
Teaming with nClouds, Onriva began the build phase of developing a machine learning model, by creating a dataset ready to support machine learning. This phase of the project has yielded numerous benefits:
nClouds created Jupyter notebooks for training data exploration and preprocessing, which in turn enabled a faster build of highly accurate training datasets. Now Onriva has one source of truth for their machine learning environment.
nClouds implemented Amazon SageMaker to reduce cost and complexity, while also increasing the accuracy of data labeling by bringing together machine learning with a human labeling process called active learning.
Using AWS Glue, Onriva’s data is immediately searchable, queryable, and available for ETL. It automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue is serverless, so there is no infrastructure to provision or manage.
Accenture. (2017). Accenture Interactive Personalized Marketing Index: The New Travel Experience. Retrieved from https://www.accenture.com/_acnmedia/PDF-61/Accenture-Interactive-Personalized-Marketing-Index_v2.pdf#zoom=50
Simon, Julien. (2019). Build Machine Learning Models with Amazon SageMaker. [SlideShare]. Retrieved from https://www.slideshare.net/JulienSIMON5/build-machine-learning-models-with-amazon-sagemaker-april-2019/8
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