DevOps Pracice

"DevOps is a set of practices, principles, and cultural philosophies that improves collaboration between Development (Dev) and IT Operations (Ops), aiming to shorten the system's development life cycle and provide continuous delivery of high-quality software."

Amol Malpani (CTO, Cloudaeon)

Key Use Cases
  • Rapid Software Releases: For businesses that need to release software updates frequently.
  • Infrastructure Management: Automating and managing infrastructure setup, scaling, and maintenance.
  • Quality Assurance: Ensuring that software releases are of high quality.
  • Collaborative Development:: Enabling better collaboration between teams, especially between developers and IT operations.
Key Benefits
  • Slow Release Cycles: Speeding up the time it takes to go from code to production.
  • Operational Inefficiencies: Automating manual processes and reducing errors.
  • Development and Operations Silos: Breaking down barriers between teams.
  • Inconsistent Environments: Ensuring that software runs consistently across different environments.
  • Downtime: Reducing system outages and ensuring higher availability.
What We Offer
  • Version Control: Tools like Git to manage code versions and allow multiple developers to collaborate.
  • Continuous Integration (CI): Regularly merging code changes into a main branch, ensuring code builds and passes initial tests.
  • Continuous Deployment/Delivery (CD): Automatically deploying the code changes to production or staging environments after passing CI.
  • Infrastructure as Code (IaC): Managing and provisioning infrastructure through code and automation tools.
  • Communication and Collaboration Tools: Platforms like Slack or Microsoft Teams for enhanced team collaboration.
  • Automated Testing: Using automated tools to ensure the code's functionality, performance, and security.
  • Configuration Management: Tools like Ansible or Puppet to automate the configuration of software and systems.
  • Containerization: Using containers, such as Docker, to package, deploy, and manage applications.
How We Work
  • Planning: Identify and plan the features, bug fixes, or other system changes.
  • Code: Write and version-control the code using platforms like Git.
  • Build: Compile the code into executable artifacts. Tools like Jenkins can automate this.
  • Test: Run automated tests to ensure quality.
  • Deploy: Push the code to a staging or production environment, using automated deployment tools.
  • Operate: The code runs in a production environment.
  • Monitor: Monitor application and system performance, ensuring everything runs smoothly and identifying issues.
  • Feedback: Any issues or feedback are looped back to the planning and coding stages.

Managed Services

  • ML Model Deployment Automation: Tools and processes to automate the deployment of ML models.
  • Model Versioning: Managing and versioning different iterations of machine learning models.
  • Model Monitoring: Tools to monitor the performance and accuracy of deployed ML models.
  • Infrastructure Management: Handling the setup and scaling of infrastructure required for training and deploying ML models.
  • Automated Testing for ML: Ensuring that ML models are producing the expected results.
  • Data Pipeline Automation: Tools to automate the ingestion, processing, and feeding of data into ML models.
  • Integration with Data Engineering Platforms: Seamless integration with data storage and processing platforms.
  • Security and Compliance: Ensuring that data and ML processes are secure and comply with relevant regulations.
  • Support and Training: Ongoing technical support, maintenance, and training for the client's team.

Readiness Check

In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.

Getting Started

If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.