Empowering Commercial Real Estate Professionals with AI/ML Applications
VTS relies on its new ML infrastructure to productionize ML models at scale, accelerating time to market for AI/ML applications that assist commercial real estate professionals in their work
VTS is a commercial real estate leasing and asset management software and data company. Founded by real estate professionals who have experienced the challenges facing today’s landlords and brokers, VTS delivers an easy-to-use, intuitive workflow and insights platform that empowers commercial real estate professionals to work smarter, not harder. Over 12 billion square feet of commercial real estate is managed on the VTS platform.
Challenge
VTS’s mission is to be commercial real estate’s decision-making platform, where real-time insights come to life. VTS wanted to productionize Machine Learning (ML) models more efficiently while gaining the ability to build new models iteratively using AWS services. They wanted to accelerate the time to market for ML applications, reduce human errors, and the effort from their DS team.
Solution
Provectus looked into how ML models were prototyped and evaluated at VTS, and delivered a template-based solution enabling VTS data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. The resulting coherent set of templates, with usage cookbook and extension guidelines, was applied successfully on a ML model that predicted leasing deal outcomes.
Outcome
By implementing the templates for VTS, Provectus helped significantly reduce the amount of manual activities related to bringing their PoC models to production in a cost-efficient and scalable manner, with architectural best practices. This created a strong foundation for the VTS team, enabling them to develop and deliver future ML applications much faster and at scale.
Accurate ML Models Deployed in Production
ML Model Productionization Templates Delivered in 3 Months
3x Faster, At-Scale Delivery of ML Models Due to Reductions in Manual Work Volume
The Challenge of Bringing Machine Learning Models to Production Without ML Engineers
One of the ML models the VTS data science team prototyped was around predicting leasing deal outcomes. By surfacing these predictions to customers, landlord reps and asset managers could make more informed decisions around their leasing and investment strategies.
However, VTS faced hurdles when integrating the predictive model into the core user experience. The data scientists were capable of delivering the model in ad hoc environments (e.g. Jupyter notebooks) but found it challenging to deploy the model in production using the existing infrastructure of the VTS platform. In short, VTS had superb data scientists but lacked the necessary AWS and MLOps expertise to finish the job.
As a result, VTS joined forces with Provectus, an AWS Premier Consulting Partner, to deliver on these objectives in an expedited manner.
From Machine Learning Models in Notebooks to Template-Based Model Productionization
To reduce these complexities, Provectus suggested that the data scientists could use VTS-specific templates and SDK to create jobs, pipelines, and endpoints in Amazon SageMaker and other AWS services. The templates and SDK would be designed, built, and delivered by Provectus, and the VTS data science and ML engineering team would receive extensive training and guidelines on how to use the templates and SDK.
The first set of templates provide the foundation for refactoring data ingestion, data preprocessing, feature engineering and model training scripts into Amazon SageMaker jobs. The delivered SDK, called VTS ML Commons SDK, extends Amazon SageMaker SDK with components tailored for VTS environments. It reduces the amount of boilerplate code and hides configuration complexity to be provided by data scientists for using Amazon SageMaker capabilities. It also encourages clear separation of data and ML processing logic from the underlying infrastructure.
The second set of templates uses this foundation to simplify connecting different workflow steps into Amazon SageMaker pipelines for training, batch-mode, and real-time model serving, and integration with other AWS services like Amazon EventBridge or AWS Lambda.
Provectus also prepared the required documentation and held several knowledge transfer sessions with VTS ML engineers, to demonstrate the speed and efficiency of using the templates instead of the manual process.
ML Productionization Templates Reduce Complexities for Data Scientists and Facilitate the Delivery of ML Applications
With the help of Provectus, VTS received the tools needed to make great strides in their ML work, from deploying existing models to developing and operationalizing new models.
The deal outcomes model was deployed in production in an agile framework, with close collaboration between the Provectus and VTS teams. The templates and SDK for the development and operationalization of ML models were also delivered by Provectus within three months.
The introduction of the templates into the ML delivery process allowed the VTS data science team to significantly reduce the amount of manual work required to productionize future ML models. By automating a part of their pipeline, the data scientists were able to prioritize high-value tasks like model building, experimentation, and hypothesis checking, instead of spending time handling routine tasks for PoC operationalization.
The usage cookbook and extension guidelines provided by Provectus also ensured a smooth transition to a new ML delivery process, which allowed VTS leaders to see actionable results from day one.
All of these Provectus’ deliverables have enabled VTS to develop and deliver future ML applications much faster, more efficiently, and on a larger scale, accelerating the company’s mission to deliver data-driven decision making to the commercial real estate industry, and bringing the sector into the quantitative era.
Moving Forward
- Learn more about the Provectus Machine Learning Infrastructure
- Watch the webinar on MLOps and reproducible ML on AWS
- Apply for Machine Learning Infrastructure Acceleration Program to get started
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