Provectus Case Studies ML Infrastructure for Commercial Real Estate Insights Platform: A Case Study on VTS

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	ML Infrastructure for Commercial Real Estate Insights Platform: A Case Study on VTS Provectus

ML Infrastructure for Commercial Real Estate Insights Platform: A Case Study on VTS

Provectus

Technology Category	Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Middleware, SDKs & Libraries
Applicable Industries	Cement Education
Applicable Functions	Logistics & Transportation
Use Cases	Last Mile Delivery Leasing Finance Automation
Services	Data Science Services Training
Challenge	VTS, a commercial real estate leasing and asset management software and data company, aimed to become the decision-making platform for the commercial real estate industry. To achieve this, they wanted to efficiently productionize Machine Learning (ML) models and build new models iteratively using AWS services. Their goal was to accelerate the time to market for ML applications, reduce human errors, and lessen the effort from their Data Science (DS) team. One of the ML models they prototyped was designed to predict leasing deal outcomes. However, they faced challenges in integrating this predictive model into the core user experience. While their data scientists were capable of delivering the model in ad hoc environments, they found it difficult to deploy the model in production using the existing infrastructure of the VTS platform. In essence, VTS had excellent data scientists but lacked the necessary AWS and MLOps expertise to complete the task. Read More
About Customer	VTS is a commercial real estate leasing and asset management software and data company. It was 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. Their mission is to be the decision-making platform for the commercial real estate industry, where real-time insights come to life. Read More
Solution	To overcome these challenges, VTS partnered with Provectus, an AWS Premier Consulting Partner. Provectus delivered a template-based solution that enabled VTS data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. They designed, built, and delivered VTS-specific templates and SDK to create jobs, pipelines, and endpoints in Amazon SageMaker and other AWS services. The first set of templates provided 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, extended Amazon SageMaker SDK with components tailored for VTS environments. The second set of templates simplified 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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Application Infrastructure & Middleware - Middleware, SDKs & Libraries

Applicable Industries

Cement

Education

Applicable Functions

Logistics & Transportation

Use Cases

Last Mile Delivery

Leasing Finance Automation

Services

Data Science Services

Training

Challenge

VTS, a commercial real estate leasing and asset management software and data company, aimed to become the decision-making platform for the commercial real estate industry. To achieve this, they wanted to efficiently productionize Machine Learning (ML) models and build new models iteratively using AWS services. Their goal was to accelerate the time to market for ML applications, reduce human errors, and lessen the effort from their Data Science (DS) team. One of the ML models they prototyped was designed to predict leasing deal outcomes. However, they faced challenges in integrating this predictive model into the core user experience. While their data scientists were capable of delivering the model in ad hoc environments, they found it difficult to deploy the model in production using the existing infrastructure of the VTS platform. In essence, VTS had excellent data scientists but lacked the necessary AWS and MLOps expertise to complete the task.

About Customer

VTS is a commercial real estate leasing and asset management software and data company. It was 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. Their mission is to be the decision-making platform for the commercial real estate industry, where real-time insights come to life.

Solution

To overcome these challenges, VTS partnered with Provectus, an AWS Premier Consulting Partner. Provectus delivered a template-based solution that enabled VTS data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. They designed, built, and delivered VTS-specific templates and SDK to create jobs, pipelines, and endpoints in Amazon SageMaker and other AWS services. The first set of templates provided 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, extended Amazon SageMaker SDK with components tailored for VTS environments. The second set of templates simplified 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.

Impact #1	The implementation of the templates for VTS significantly reduced 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. The deal outcomes model was deployed in production in an agile framework, with close collaboration between the Provectus and VTS teams. 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. 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.

Impact #1

The implementation of the templates for VTS significantly reduced 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. The deal outcomes model was deployed in production in an agile framework, with close collaboration between the Provectus and VTS teams. 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. 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.

Benefit #1	ML Model Productionization Templates Delivered in 3 Months
Benefit #2	3x Faster, At-Scale Delivery of ML Models Due to Reductions in Manual Work Volume
Benefit #3	Significant reduction in manual activities related to bringing PoC models to production

Benefit #1

ML Model Productionization Templates Delivered in 3 Months

Benefit #2

3x Faster, At-Scale Delivery of ML Models Due to Reductions in Manual Work Volume

Benefit #3

Significant reduction in manual activities related to bringing PoC models to production

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Overview

ML Infrastructure for Commercial Real Estate Insights Platform: A Case Study on VTS

Operational Impact

Quantitative Benefit