![\"Travtus](\"https:\/\/devoteam.info\/wp-content\/uploads\/2025\/04\/stuy-town.jpeg\")
<\/a><\/figure>\n\n\t\t\n\t\tWith their long-standing experience in Real Estate Operations and with the latest research in Deep Learning and Conversational Interface design, they bring automation to Real Estate and are the creators of a leading Artificial Intelligence teammate Adam, who enables Real Estate companies to provide exceptional customer service.<\/p>\n\n\n\n
Adam helps to bring efficiency and automation across all areas of Property Management, by engaging with customers in the way they prefer. From email and text, to web chat and apps, Adam helps customers access the services they need 24 x 7 x 365.<\/p>\n\n\n\n
The Challenge<\/h2>\n\n\n\n
Travtus\u2019 product Adam<\/a> is an AI teammate for real estate. It uses machine learning to automate multifamily property management, reviewing resident communications to gather intelligence on the property.<\/p>\n\n\n\n Preparing and managing training data, MLP Ops and Production model monitoring are time consuming activities and Travtus were keen to explore Amazon SageMaker to determine its strengths and capabilities, and the activity and cost of migrating to this service.<\/p>\n\n\n\n As part of the program of works, several pain points in the current state architecture were identified. These areas of concern served as the basis for the design and scope of a proof-of-concept project.<\/p>\n\n\n\n Amazon SageMaker<\/a> features make it easier for developers to automate and scale all steps of the end-to-end machine learning workflow. This is enabled through powerful capabilities such as:<\/p>\n\n\n\n With this in mind a proof-of-concept (PoC) project was agreed with the goal of demonstrating the value of SageMaker to Travtus and how it could help solve key problems for the business.<\/p>\n\n\n\n The scope of the project was a pre-trained PyTorch text classification model with SageMaker pipelines. The objective was to demonstrate how to execute model training and development workflow using Amazon SageMaker. The method employed was to use a toy problem of finetuning a pre-trained Pytorch text classification model on a data set provided by Travtus. The trained model would be hosted as a serverless service, which was built into Lambda execution functions in Service Accounts. This showed how SageMaker pipelines brings CI\/CD best practices into Machine Learning. This feature was demonstrated through a SageMaker Studio Notebook for ease of use. The second part of the project was to alter the pipeline to a pre-trained model \u2013 again, trained on a Travtus toy data set. This trained model was hosted as a real-time endpoint, which was then built into Lambda execution functions.<\/p>\n\n\n\n Travtus were keen to explore SageMaker to understand its feasibility, and quantify costs, time and complexity benefits that can be leveraged from migrating to the service. Based on the challenges defined at the start of the project, the following outcomes were delivered:<\/p>\n\n\n\n During the proof-of concept project we were able to solve all the agreed problem areas, and delivered the immediate benefits:<\/p>\n\n\n\n The success Travtus achieved highlights the power of a cohesive machine learning platform. A central component that enables this kind of streamlined development is the environment itself. To understand the technology that brings these capabilities together, explore our deep dive into SageMaker Unified Studio and its centralised access to data, AI, and governance<\/a>.<\/p>\n\n\n\n Partner with Devoteam to leverage award-winning tech expertise, agile execution, and a culture of continuous learning.<\/p>\n<\/div>\n\n\n\nThe Solution<\/h2>\n\n\n\n
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The Benefits<\/h4>\n\n\n\n
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<\/div>\n\n\n\n<\/span><\/div>\n\n\nYour Success Starts Here<\/strong><\/h2>\n<\/div>\n\n\n\n
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