Integrating DevOps Into Software Development

You must approach a data platform in three layers: 1.) Code - What engineers care about, 2.) Data - What analysts care about, 3.) Business Logic - what business users care about. If you do not have a multi-tiered approach that captures the RIGHT information from each layer, your data platform and strategy will be an incoherent mess that struggles to gain adoption. Each persona in your audience will only have a partial amount of the information they need, and your stakeholders will constantly demand new things that feel disjointed. Cataloging, Monitoring, Lineage, and Data Contract are not TOOLS. They are patterns. And each pattern has a different application and use case depending on the persona. For example, most Software Engineers do NOT care about the data itself. They work with software and code. What they are willing to own is the code that produces data, and systems/software that allows them to manage their data code in a more structured way. Therefore, a catalog that only focuses on data is less than useless for a software engineer because they have no context on how to apply this to their day-to-day work and adds significant overhead. This is where Data DevOps is critical: - Data Contracts are enforced in CI/CD and prevent backward incompatible changes like integration tests and unit tests for data code - The Catalog captures code owners. Engineers who manage repos that produce ultimately produce data, the repo list, who has made changes over time, events, and other sources - Code-based lineage focuses on how code moves data across services as a dependency graph - Monitors exist to help teams understand when new data code is being created, if its following the expected patterns, and how data code is being changed If engineers do not have a Data DevOps system, they will NEVER adopt (or push back strongly) against a system that requires they take ownership of the data itself. Asking an engineer to own data, without first helping them own the code that produces the data is totally backwards. So in short - don't buy a cataloging tool, or a data contract tool, or a monitoring tool. These are features that enable a particular workflow for a certain group of people within your business. Once your platform begins to view this functionality as layers that must work together cohesively, your platform initiative will explode in terms of adoption and value. Good luck!

If I were advancing my DevOps skills in this AI-driven era, understanding the MLOps process would be my starting point (also knowing the DevOps role in each stage) Let's break down what you need to know: 1. Data Strategy: Define goals and data needs for the ML project. ↳ DevOps Role: Provides infrastructure and tools for collaboration and documentation. 2. Data Collection: Acquire data from diverse sources, ensuring compliance. ↳ DevOps Role: Sets up and manages data pipelines, storage, and access controls. 3. Data Validation: Check quality and integrity of collected data. ↳ DevOps Role: Automates validation processes and integrates them into data pipelines. 4. Data Preprocessing: Clean, normalize, and transform data for training. ↳ DevOps Role: Provides scalable compute resources and infrastructure for preprocessing. 5. Feature Engineering: Create meaningful inputs from raw data. ↳ DevOps Role: Supports feature stores and automates feature pipeline deployment. 6. Version Control: Manage changes in data, code, and model setups. ↳DevOps Role: Implements and manages version control systems (Git) for code, data, and models. 7. Model Training: Develop models with curated data sets. ↳DevOps Role: Manages compute resources (CPU/GPU), automates training pipelines, and handles experiments (MLflow, etc.). 8. Model Evaluation: Analyze perf metrics. ↳DevOps Role: Integrates evaluation metrics into CI/CD pipelines and builds monitoring dashboards. 9. Model Registry: Log and store trained models with versions. ↳DevOps Role: Sets up and manages the model registry as a central artifact store. 10. Model Packaging: Bundle models and dependencies for deployment. ↳DevOps Role: Automates the containerization of models and their dependencies. 11. Deployment Strategy: Outline roll-out processes and fallback plans. ↳DevOps Role: Leads the design and implementation of deployment strategies (Canary, Blue/Green, etc.). 12. Infrastructure Setup: Arrange compute resources and scaling guidelines. ↳DevOps Role: Provisions and manages the underlying infrastructure (cloud resources, Kubernetes, etc.). 13. Model Deployment: Move models into the production environment. ↳DevOps Role: Automates the deployment process using CI/CD pipelines. 14. Model Serving: Activate model endpoints for application use. ↳ DevOps Role: Manages the serving infrastructure, scaling, and API endpoints. 15. Resource Optimization: Ensure compute efficiency and cost-effectiveness. ↳ DevOps Role: Implements auto-scaling, cost management strategies, and infrastructure optimization. 16. Model Updates: Organize re-training and version advancements. ↳DevOps Role: Automates the retraining and redeployment processes through CI/CD pipelines. It's a steep learning curve, but actively working on MLOps projects and understanding these stages is absolutely vital today.. 🔔 Follow Vishakha Sadhwani for more cloud & DevOps content. ♻️ Share so more people can learn. Image source: Deepak Bhardwaj

Reflecting on Agile Development with DevOps 2.0: A Flexible CI/CD Flow Last year, I shared a CI/CD process flow for Agile Development with DevOps 2.0, and it’s been amazing to see how much it resonated with the community! This framework isn’t about specific tools—it’s about creating a seamless, collaborative process that supports quality and agility at every step. ✅ 𝗣𝗹𝗮𝗻: Building a Strong Foundation with Clear Alignment The journey begins with planning—whether it's user stories, tasks, or broader product goals. Tools like JIRA or Asana (or any project management platform) help capture requirements and align the team with the Product Owner’s vision. This early alignment is essential to avoid misunderstandings and establish a shared understanding of success. Key Insight: Planning thoroughly and involving stakeholders from the start leads to a smoother process. When everyone’s on the same page, the entire pipeline benefits. ✅ 𝗖𝗼𝗱𝗲: Collaborative Development and Real-Time Feedback In the coding phase, developers work together, often pushing code to a version control platform like GitHub or Bitbucket and communicating via real-time collaboration tools like Slack or Teams. Open communication and continuous feedback help catch issues early and keep the team in sync. Key Insight: Real-time feedback is crucial for speed and quality. Regardless of the tools, creating a culture of continuous collaboration makes all the difference. ✅ 𝗕𝘂𝗶𝗹𝗱: Automating Quality and Security Checks As code is committed, it’s essential to automate quality and security checks. Tools like Jenkins, CircleCI, or any CI/CD platform can trigger builds and run automated tests, ensuring that quality checks are consistent and fast. This step helps prevent issues from creeping into production. Key Insight: Automated checks for quality and security are invaluable. Integrating these checks into the build process improves confidence in every deployment. ✅ 𝗧𝗲𝘀𝘁: Structured, Multi-Environment Testing Testing is layered across environments—whether it’s regression, unit, or user acceptance testing (UAT). Using frameworks like Selenium for automated testing or dedicated QA/UAT environments enables rigorous validation before production. Key Insight: Testing across environments is a safeguard for quality. Structured testing helps ensure that code is reliable and ready for release. ✅ 𝗥𝗲𝗹𝗲𝗮𝘀𝗲: Scalable, Reliable Deployments with Infrastructure as Code (IAC) Finally, using Infrastructure as Code (IAC) principles with tools like Terraform, Ansible, or other IAC solutions, deployments are made repeatable and scalable. IAC empowers teams to manage infrastructure more efficiently, ensuring consistent and controlled releases. Thank you to everyone who has engaged with this diagram and shared your insights! I’d love to hear how others approach CI/CD. Are there any tools or strategies that have worked well for you?