Machine Learning Platform

Designed to support the end-to-end ML workflow: manage data, train, evaluate,
 and deploy models, make predictions, and monitor predictions

About a Machine Learning Platform

The AI Singapore technology stack includes incorporating components described below as well as others into an integrated platform to support our data scientists and AI engineers. It is continuously evolving to incorporate new technologies and to address new challenges in machine learning.

The technology stack is comprised of tooling and pipelines for data scientists to develop, deploy, measure, improve and iterate to enable better models not only for one particular problem but wider range of datasets and algorithms. It also includes all the requirements of more traditional applications such as API development and versioning, package management, containerization, reproducibility, scale, monitoring, logging and more.

Common Tools and Libraries


Polyaxon is an open source platform for building, training, and monitoring large scale deep learning applications.

Developer's Resouce:

Apache Spark

Apache Spark is an open-source distributed general-purpose cluster computing framework with (mostly) in-memory data processing engine

Developer's Resource:


Seldon Core is an open source platform for deploying machine learning models on a Kubernetes cluster.

Developer's Resource:

100E Use Cases

Model management tools and frameworks support all current 100E projects (see diagram below), enabling both data scientists and AI Engineers to create data pipelines, engineer features, train models and monitor and manage the lifecycle of those models.

Related Articles

  1. Distributed Deep Learning with Polyaxon
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  2. Spark Tutorial
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  3. Manage ML Deployments Like A Boss: Deploy Your First AB Test With Sklearn, Kubernetes and Seldon-core using Only Your Web Browser & Google Cloud
    Link to article: