GICT Certified Microservice with AI Specialist course (CMAIS) will allow participants to learn why they are well-suited to modern cloud environments that require short development and delivery cycles. Trainees will also be taught the characteristics of Microservices, its comparison with monolithic style, technology choices, and Microservice architecture.
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 microservice 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
Objective of the Microservice with AI 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.
Microservices deployed in the back-end cloud infrastructure enable developers to focus on modelling and coding for Machine Learning. Though this course participant 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.
The participants will be able to appreciate how the typical workloads in Machine Learning which need specialized hardware like GPU can be handled by a platform such as Kubernetes and with an open source project Kubeflow. Kubeflow handles the Machine Learning stack for Kubernetes. Finally, as developers face the heterogeneous multi-clouds environments and AI tools, they should also demand industry-standard DevOps toolchain for building, deploying, and be optimizing micro-serviceswhich are covered in this course.
- 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
- 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
- 4-day intensive training program
Participants are preferred to have experience in software development, business domain or data/business analysis.
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