Use Cases

To-date, MAESTRO is serving the AI/ML needs of more than 450 users from over 70 project teams across 30 public agencies on a diverse range of use cases – from enhancing service delivery to improving workspace productivity. Agencies are leveraging the platform to productionise their AI/ML models efficiently, allowing them to continuously develop, test and deploy models through the automation of ML and CI/CD pipelines. As a readily available AI/ML Ops environment built in compliance with government architecture and security standards, any agency can work on their data projects securely right away without the need for them to expend additional time and effort to develop equivalent systems on their own.

Adopting full MLOps workflow on GCC 2.0

MAESTRO enables the Housing Development Board (HDB) to adopt full MLOps workflow on GCC 2.0 via AWS SageMaker, together with an R Model Template developed in-house by the MAESTRO engineering team, providing an End-to-End solution where the Model can be developed and maintained within the SageMaker environment but readily consumed externally in the government network via API Gateway configurations. Such automation leads to time savings of approximately 26 man-days (0.5 man-day per week, 52 weeks in a year) per year by not manually retraining the models that MAESTRO can help achieve for them. Aside from developmental cost avoidance, HDB also saves a total of 52 man-days per year on preventive maintenance (~12 man-days ) and annual upgrade cycles (~40 man-days), if they were to continue with their existing ML platform that utilises on-premise infrastructure.

Productionising key ML use cases

MAESTRO enables SkillsFuture Singapore (SSG) to productionise their key ML use cases seamlessly by providing a secure, enterprise-ready MLOps environment. As the implementation of such an environment requires substantial amounts of money, SSG avoids significant costs by not having to set up and maintain their own infrastructure by leveraging MAESTRO directly. Since MAESTRO is a readily available platform, it also helped SSG to accelerate their productionisation plan, as they previously estimated that it would take up to ~6 man months to develop a similar system to what MAESTRO offers before they can start on their use cases.

Accelerating and scaling Large Language Model (LLM) use case development

MAESTRO enables the Ministry of Manpower (MOM) to accelerate and scale their LLM use case development that was previously restricted by their on-premise set-up, which had limited functionality and compute resources. Since MAESTRO has egress connectivity with Container Stack via Government Enterprise Network, leveraging MAESTRO’s platform enables MOM to deploy these LLM use cases for its end-users on GCC, which was previously not possible with the on-premise setup. Key use cases include (a) CRM Sensemaker Query Management (end user benefit was to reduce time spent on analysis by 75%) and (b) SSOC Autoencoder (end user benefit was to improve the accuracy of SSOC labels from 40% to 70%).