Tag: Systems Engineering

DevOps Skills

The broad picture. Skills to address the “from Code to Infrastructure” paradigm. Bridging ends from code producers to deployment in production – mindset of all involved, get a sense of the process as well do the automation of it and the orchestration and monitoring.

Collaborate with internal management teams involved in the DevOps process and stay familiar with the objectives, roadmap, blocking issues and other project areas.
Have the skills to mentor and advise team members on the best ways to deliver code, what tools to use when coding and how to test the latest features.

The target. Fast provisioning: be able to setup new machines fast. Good monitoring: to be quickly able to diagnose failures and trace them down. Quickly rollback to a previous version of the microservice. Rapid app deployment through fully automated pipelines. Create the Devops mindset / culture.

DevOps engineers need to know how to use and understand the roles of the following types of tools:
1. Version control: GitHub, GitLab
2. Continuous Integration servers: code coming in repository server and triggers build and doc: Jenkins, GitLab CI, Atlassian Bamboo, Circle CI, GitHub Actions
3. Configuration management: Software Configuration Management SCM Tools: Configuration management occurs when a configuration platform is used to automate, monitor, design and manage otherwise manual configuration processes. System-wide changes take place across servers and networks, storage, applications, and other managed systems: Puppet, Ansible, Chef
4. Deployment automation: Ansible Tower, Bamboo
5. Containers: containerd, Docker, Artifactory
6. Infrastructure Orchestration: automating the provisioning of the infrastructure services needed to support an app moving into production – in the right order, is orchestration: Terraform, Ansible (also Config. Management Tool), Chef, Kubernetes
7. Monitoring and analytics: Prometheus, Datadog, Splunk
8. Testing and Cloud Quality tools: a test automation platform uses scripts to automate the whole process of software testing. Identify the tests that need to be automated. Research and analyze the automation tools that meet your automation needs and budget. Based on the requirements, shortlist two most suitable tools. Do a pilot for two best tools and select the better one. Discuss the chosen automation tools with other stakeholders, explain the choice, and get their approval. Proceed to test automation
Tools: Kobiton, Eggplant, TestProject, LambdaTest
9. Network protocols from layers 4 to 7, nginx, caching, Service Mesh.
10. Programming skills with Java, Shell, Python, JS, Ruby…

Also:
Monitoring production environments
Performance measurements
Security
Cloud administration
Get proper alerts when something is wrong or unavailable
Help resolve problems either through online support or technical troubleshooting

Production Support with AI

In the industrial sector, AI application is supported by the increasing adoption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehicles, or devices carried by human workers generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance or performance optimization at unprecedented levels of accuracy. Hence, the combination of IoT and AI is expected to kick off the next wave of performance improvements, especially in the industrial sector. AI-equipped controllers are meant to immediately detect signs of equipment irregularity by monitoring the status of equipment and processes and assure product quality.

Business Support with AI

Finance, HR, and IT are key to ensuring a business’ effective operation. They could benefit from AI in form of Expert Systems, IT Agents trained in the specific business environment capable of solving more complex problems. This is achieved with natural language processing and reinforced learning like Google’s AlphaGo.