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
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.
- Distributed Deep Learning with Polyaxon
Link to article: https://medium.com/polyaxon/distributed-deep-learning-with-polyaxon-6d9f1288e4b8
- Spark Tutorial
Link to article: https://www.edureka.co/blog/spark-tutorial/
- 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: https://medium.com/analytics-vidhya/manage-ml-deployments-like-a-boss-deploy-your-first-ab-test-with-sklearn-kubernetes-and-b10ae0819dfe