Features & Roadmap

MAESTRO provides MLOps capabilities through Amazon SageMaker Studio. Using ML and CI/CD pipelines, agencies can now train, deploy and monitor models at scale with standardised workflows, boosting the productivity of data science teams while maintaining model performance and quality in production.

Through Amazon SageMaker Jumpstart, MAESTRO also provides users with access to a selection of pre-trained foundation models, along with pre-built algorithms and ML solutions that can be deployed with a few simple clicks. Additionally, MAESTRO also allows users to host quantised models for resource optimisation and efficiency.

As for Generative AI services, MAESTRO provides access to AWS Bedrock that is exclusively hosted in Singapore region, allowing users to leverage models such as Claude 3.5 Sonnet and & Claude 3 Haiku via APIs. Besides AWS Bedrock, MAESTRO also allows users to call up-to-date models like GPT 5 from other clouds like Azure through APIs.

Update: do note that MAESTRO's AI API service will eventually be provisioned via PlatformAI - users will need to generate their API keys there for use within our platform. Stay tuned for future updates.

MAESTRO is integrated with SHIPHATS GitLab and Nexus Repository for teams and agencies to share and collaborate seamlessly. SHIPHATS Nexus Repository allows users to get the latest versions of open-source Python/R libraries into their Jupyter Notebook environment, and MAESTRO also offers custom runners for users to trigger their workflows through SHIPHATS Gitlab.

MAESTRO is integrated with central WOG data sources like Datahive and Data.gov, allowing users to download and access approved data by simply running curl commands within the Jupyter Notebook. 

Besides, MAESTRO is integrated with Cloak, a central privacy toolkit that helps users apply data transformation techniques and PII detection/anonymisation. Users can access Cloak's APIs and download transformed data directly from MAESTRO’s environment.

In addition, MAESTRO is also integrated with Container Stack and Airbase to facilitate the deployment of AI/ML models to Intranet applications. Other WOG service integrations include Datahive, Data.gov, APEX, AI Bots, Ownself Gather, AISAY, OneMap, Fraud Detection Platform, as well as Sentinel.

Finally, MAESTRO is able to establish data connectivity with agencies’ data systems, as which is an important requirement for agencies who wish to implement end-to-end AI/ML Ops. The technical mode of connectivity would have to be assessed accordingly on a case-by-case basis due to the different nature of architectural set-ups across various agency systems.

MLflow is an open-source platform that streamlines the machine learning lifecycle, including experiment tracking, model versioning, deployment, and monitoring.

MAESTRO provides a managed MLflow environment for teams within the same domain, without requiring users to manually provision infrastructure. Our platform handles the underlying backend resources, allowing users to focus on experimentation, model development, and tracking results through a shared MLflow Tracking Server.