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Certified Microservice with AI Specialist (CMAIS) Course

Funding-GICT-Training

Certification in
AI/ML, Big Data, Cloud Computing & IoT

Up to 90% funding for SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

Register Now More Info
SkillsFuture Course in Singapore

Up to 90% funding for
SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

Certification in
AI/ML, Big Data, Cloud Computing & IoT

Register Now

Objective of the Microservice with AI Training

The Microservice with AI training on advanced computing with AI explains Microservices, its orchestration and how Machine Learning will be a natural fit for Microservice architecture and container orchestration. Participants will acquire knowledge on the characteristics of Microservices, its comparison with monolithic style, technology choices, and Microservice architecture.

Microservice with AI Training

The training will also explain Microservice, its orchestration and how Machine Learning will be a natural fit for Microservice architecture and container orchestration. Participants will be able to containerize application by creating Docker configuration files and use the open source container orchestration tool Kubernetes, for automating deployment and management of containers.

Importance of Microservice with AI Training

Microservices is considered as the next generation services architecture that addresses the pain points associated with the traditional enterprise Service Oriented Architecture. The global market for Microservices is expected to grow at approx. USD 33 Billion by 2023, at 17% of CAGR between 2017 and 2023. These developments are partly because of the work at companies such as Netflix, Amazon, and e-Bay which have visibly applied Microservices. In fact, these companies have not only adopted Microservices practices, but they have also shared the insights and even tools with the tech community. The growth of public cloud services provided by enterprise companies such as Amazon, Microsoft, and Google have put an emphasis on the “as a service” business model that allows companies to pick and choose necessary Microservices. With advanced cloud computing on AI, businesses can enhance product capabilities, better interact with customers, and create predictive business strategies.

Course Outline
  • Unit 1: Micro services architecture
  • Unit 2: Containers with Docker and Docker concepts
  • Unit 3: DevOps
  • Unit 4: Kubernetes
  • Unit 5: Kubeflow
  • Unit 6: TensorFlow in production – TensorFlow serving
Course Outcome
  • Understand the evolution of a software architecture from monolithic tomicro services
  • Understand micro service architecture and its importance in Machine Learning
  • Introduction to the open source Kubernetes by Digital Ocean
  • Understand and implement in Kubeflow - The Machine Learning stack for Kubernetes.
  • Design and develop a microservice application using Docker and orchestrate using Kubernetes
  • Design and develop TensorFlow serving to launch TensorFlow model inproduction
  • Design and demonstrate TensorFlow predictive model microservice
Duration
  • 4-day intensive training program
Pre-Requisites
  • Participants are preferred to have experience in software development, business domain or data/business analysis.
Funding
  • CITREP+
  • SkillsFuture
  • NTUC Training Fund (SEPs)
  • IBF FTS Funding Support

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