Overview Course

This course on advanced computing with AI explains microservices, its orchestration and how Machine Learning will be a natural fit for Microservice architecture and container orchestration. Through this course, participants will learn why microservices are well-suited to modern cloud environments that require short development and delivery cycles. Participants will be taught the characteristics of microservices, its comparison with monolithic style, technology choices, and microservice architecture.

Learning Outcome

  1. Understand the evolution of a software architecture from monolithic to micro services
  2. Understand micro service architecture and its importance in Machine Learning
  3. Introduction to the open source Kubernetes by Digital Ocean
  4. Understand and implement in Kubeflow - The Machine Learning stack for Kubernetes
  5. Design and develop a microservice application using Docker and orchestrate using Kubernetes
  6. Design and develop TensorFlow serving to launch TensorFlow model in production
  7. Design and demonstrate TensorFlow predictive model microservice

Who should Attend?

  • Data Analyst – Statistics and Mining
  • Data Analyst – Text Analytics
  • Operations Research Analyst
  • Senior Data Analyst- Statistics and M

Eligibility Criteria

  • Participants are preferred to have experience in software development, business domain or data/business analysis.
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information as accurate as of 6 August 2020