TED/C04
Cloud, DevOps and Machine Learning
12 months, Rs. 2.475 lakhs, AWS Cloud, Azure Cloud, Google Cloud, Github, Bitbucket Jenkins, DevOps, Machine learning, Tensorflow, Hands-on
Students learn Cloud and DevOps using Hands on Training, Students learn Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on AWS cloud, Google cloud, Azure cloud.Students will also be trained to develop Machine learning models in Azure, AWS and Google cloud.
The student will be a Cloud Enabled Certified CloudOps Professional.
TED/C04
Number of Lecture (L) hours and Practical (P) hours
- All subjects - 20% theory + 80% Lab sessions
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for cloud ( 3 calendar months)
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for DevOps ( 3 calendar months)
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for Machine learning and bigdata ( 3 calendar months)
- Assignments, min projects small internships - total 60 days ( 3 calendar months)
Differentiators
- Training delivered by Consultants who do Cloud and DevOps in enterprise customer segment.
- More architectural knowledge with hands-on experience to back up the architect is not just theory but also implement and run the services.
- This is as good as 6 months of job experience instead of mere academic course.
- Students learn Multi-cloud ( AWS, Azure and Google cloud ) that way has a higher rate of selections in companies. Example: hiring person looking for azure can hire, strictly if hiring for AWS cloud also can or even google cloud hiring manager
- Machine learning course also delivered by the live practitioner and a Data scientists.
Evaluation
Three exams:
- Theory- 100 marks
- Objective- 100 marks
- Practical ( 4 Scenario based questions, 25 marks each) 100 marks
Student should score a minimum of 60 marks (aggregate)
This exam is done for every 4 months during the 12 month course
Reference Materials
In the brochure
Course Learning Outcomes
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on AWS cloud.
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on Azure Cloud.
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on Google cloud.
- Setup a production grad fully function CI/CD pipeline using Github/bitbucket/, Jenkins/Bamboo CI, sonarqube, nexus, owsap, ansible, terraform chef and puppet.
- Deploy docker and Kubernetes cluster, helm charts , Prometheus and Grafana for Kubernetes monitoring.
- Ability to build you ML model using TensorFlow.
- Ability to use trained ML models in Azure cloud.
- Ability to use trained ML models in AWS cloud.
- Ability to use trained ML models in Google cloud.
Pre- requisites
- Basics of Linux is mandatory. (mandatory skills and not optional)
- Basics of one more language. (mandatory- however academic knowledge is sufficient)
- Basics of Python programming. (desired, but some basics will be covered by us)
Teaching pedagogies
- Training delivered by practitioners and not academic people
- Focus on hands-on labs rather than theoretical
- Get trained exactly, what Industry is looking for.
- We challenge you to get this curse reviewed by any Cloud and DevOps expert and let us know if they don't think our course is world-class curriculum and exactly what industry demands.
- World-class Machine learning models on AWS, Azure and Google Cloud.
Tools Used
- AWS Cloud
- AzureCloud
- Googlecloud
- Github
- Bitbucket
- Jenkins
- BambooCI
- SonarQube
- Nexus
- Jira
- Ansible
- Chef
- Puppet
- Docker
- kuberentes
- helm
- Prometheus
- Grafana
- Tensorflow
- ML models
- Azure ML
- Google cloud ML
- AWS cloud ML
Placement Potential
All IT roads are heading towards Cloud and DevOps, be it clients investing there new IT investments are predominant to cloud, system integrators and services company investing predominantly on building their team to deploy, migrate and manage cloud and DevOps services. In addition, adding intelligence to your application is fulfilled with machine learning, the more you understand customers better you can sell. Machine learning helps these areas a lot. ValuePoint Systems and many SI’s are interested in hiring.
Industry Associates
Cloud enabled
Pricing
INR 2,47,500 per student (excluding taxes)
Number of Lecture (L) hours and Practical (P) hours
- All subjects - 20% theory + 80% Lab sessions
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for cloud ( 3 calendar months)
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for DevOps ( 3 calendar months)
- 72 hours (theory) + 288 hours (Lab session) - total 60 days for Machine learning and bigdata ( 3 calendar months)
- Assignments, min projects small internships - total 60 days ( 3 calendar months)
Differentiators
- Training delivered by Consultants who do Cloud and DevOps in enterprise customer segment.
- More architectural knowledge with hands-on experience to back up the architect is not just theory but also implement and run the services.
- This is as good as 6 months of job experience instead of mere academic course.
- Students learn Multi-cloud ( AWS, Azure and Google cloud ) that way has a higher rate of selections in companies. Example: hiring person looking for azure can hire, strictly if hiring for AWS cloud also can or even google cloud hiring manager
- Machine learning course also delivered by the live practitioner and a Data scientists.
Evaluation
Three exams:
- Theory- 100 marks
- Objective- 100 marks
- Practical ( 4 Scenario based questions, 25 marks each) 100 marks
Student should score a minimum of 60 marks (aggregate)
This exam is done for every 4 months during the 12 month course
Reference Materials
In the brochure
Course Learning Outcomes
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on AWS cloud.
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on Azure Cloud.
- Customer app, network, compute, storage infrastructure, architecting, deploying and monitoring on Google cloud.
- Setup a production grad fully function CI/CD pipeline using Github/bitbucket/, Jenkins/Bamboo CI, sonarqube, nexus, owsap, ansible, terraform chef and puppet.
- Deploy docker and Kubernetes cluster, helm charts , Prometheus and Grafana for Kubernetes monitoring.
- Ability to build you ML model using TensorFlow.
- Ability to use trained ML models in Azure cloud.
- Ability to use trained ML models in AWS cloud.
- Ability to use trained ML models in Google cloud.
Pre- requisites
- Basics of Linux is mandatory. (mandatory skills and not optional)
- Basics of one more language. (mandatory- however academic knowledge is sufficient)
- Basics of Python programming. (desired, but some basics will be covered by us)
Teaching pedagogies
- Training delivered by practitioners and not academic people
- Focus on hands-on labs rather than theoretical
- Get trained exactly, what Industry is looking for.
- We challenge you to get this curse reviewed by any Cloud and DevOps expert and let us know if they don't think our course is world-class curriculum and exactly what industry demands.
- World-class Machine learning models on AWS, Azure and Google Cloud.
Tools Used
- AWS Cloud
- AzureCloud
- Googlecloud
- Github
- Bitbucket
- Jenkins
- BambooCI
- SonarQube
- Nexus
- Jira
- Ansible
- Chef
- Puppet
- Docker
- kuberentes
- helm
- Prometheus
- Grafana
- Tensorflow
- ML models
- Azure ML
- Google cloud ML
- AWS cloud ML
Placement Potential
All IT roads are heading towards Cloud and DevOps, be it clients investing there new IT investments are predominant to cloud, system integrators and services company investing predominantly on building their team to deploy, migrate and manage cloud and DevOps services. In addition, adding intelligence to your application is fulfilled with machine learning, the more you understand customers better you can sell. Machine learning helps these areas a lot. ValuePoint Systems and many SI’s are interested in hiring.
Industry Associates
Cloud enabled
Pricing
INR 2,47,500 per student (excluding taxes)
