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

DeFi space

The space of future finances. The departure from the standard centralized (banks) finances model to the digital counterpart, decentralized.

Decentralized: no central authority. A kind of giving the power to the network aka. power to the people.

  • DeFi Tokens via DaFi Protocol
  • Token incentives
  • Synthetics
  • Liquidity pools
  • Futures

Existing DeFi applications include stable-coins, decentralized exchanges, and peer-to-peer lending services.

More soon…

Open letter to AI Startups and AI researchers

Update 11.2020: It is clear now that the crisis is pushing digitalization needs with an unprecedented demand across all fields and that the shape of the company, the world and economy will look very different from the one before COVID. The way we work is changing, the way we interact is changing, the way we buy and do things are also different and slowly are evolving to a new normal state.
For a company to survive in the new normal, there is an imperative need to foster an organizational culture of rapid adaptation through digitalization into all domains. Also, to create a work environment with less top-down structure of control and with silos of information, towards a team work collaboration, based on trust, with clearer purposeful aims and encouraging the teams in achieving a better emotional quotient within it. The more receptive to these aspects the companies are, the more the better the transition to the new normal will be.

Original text 03.2020: The actual COVID-19 crisis has a lot of lessons to teach us. And we have nothing learned from past pandemics. We’re just falling a downward spiral of human and economic depression. We are living with the hope that this fall will end soon and that we can recover fast. This remains to be seen but besides the many scenarios, we can say for sure that we (as EU to local administrations) were not prepared for the pandemic fight.

My criticism here is simple and only towards the AI field in which investors, CEOs, funding parties, stakeholders, must accept the failure in serving the vital interests, human needs and a failure to set these priorities. In old programming therms, it’s like we are developing a nice GUI, with no good business logic, no solid platform, obsolete middle tier connections. There is no way to advance here until we are not addressing the base layers.

Every single AI researcher should from now on follow a new AI ethic book and focus on what is essential first. The AI research field is so stretched in a multitude of domains and situations while no field excels actually. You can imagine how would have been if all the concentrated AI efforts were to be focused on health-care, fully automated hospitals and personal-care assistants & anti-virus development first. And only after that worked, to try to develop AI based sex-dolls, what a shame to all of us!

I feel myself uneasy now, regretting that I should have pursued what I started in the first place, AI medical informatics with agents that help fight addictions and the build of persuasive personal agents to help for a better healthy life of the individual. With the good AI.

In a hope that this crisis does not deepen and throw us in a new AI stone age we were just escaping, I expect that things will change, and a better evaluation of our AI priorities will be taken into consideration. Maybe a new AI authority will have to take the helm and supervise the further development.

C.Stefan – March 2020.