Building in Public: My SRE, DevOps & MLOps Documentation Series

I've spent the last few years diving deep into DevOps, Site Reliability Engineering (SRE), and Machine Learning Operations (MLOps), designing infrastructure, solving complex problems, and automating deployment workflows across cloud environments. It has been a journey filled with challenges, learning curves, and incredibly rewarding moments.

Now, I am kicking off a new phase in my career: documenting my past experiences and sharing what I learn and build in public.

Why This Series?

When I thought about documenting my work, I did not want to create just another technical blog. I envisioned something living and evolving, a space that resonates with both beginners exploring DevOps, SRE, and MLOps and seasoned engineers looking for practical, field-tested implementations.

This series is not just about what I have done; it is about how real-world systems are built, automated, and scaled, with all the nuances, lessons learned, and trade-offs included.

What You Can Expect

This series will walk through hands-on, real-world projects, including:

• CI/CD for software using Jenkins, GitHub Actions, Azure DevOps Pipelines, and Argo Continuous Delivery (Argo CD)
• Infrastructure as Code with Terraform and AWS CloudFormation
• Monitoring and alerting with Prometheus, Grafana, Datadog, and Amazon CloudWatch
• Secure container orchestration with Kubernetes (EKS and AKS) and Helm
• Configuration management with Ansible
• Observability using the Elastic Stack (formerly ELK Stack: Elasticsearch, Logstash, and Kibana) or the EFK Stack (Elasticsearch, Fluentd, and Kibana), plus Gatus
• Containerization and registries with Docker, and image management via ECR, ACR, and JFrog Artifactory
• DevSecOps for code quality and security with SonarQube, Trivy, Talisman, OWASP Dependency-Check, and Veracode

MLOps and Data Engineering

• Data pipelines and orchestration for batch and streaming workloads, for example with Airflow or cloud-native schedulers
• Experiment tracking and model registry using tools such as MLflow or Weights and Biases
• CI/CD/CT for models that automates training, validation, and gated promotion to staging and production
• Model serving on Kubernetes or managed endpoints, including canary and shadow deployments
• Monitoring for models covering performance, drift, and data quality, with automated alerts and rollback playbooks
• Feature and dataset management with versioning, lineage, and reproducibility at the core

For seamless cloud integration across Amazon Web Services (AWS) and Microsoft Azure, this series will demonstrate how to leverage core services for scalable, secure, and resilient architectures, including MLOps services such as Amazon SageMaker, AWS Glue, and Azure Machine Learning.

We will also expand into data-driven applications that connect these foundations to real outcomes.

All of this will be built on a progressively evolving base infrastructure, showcasing implementation patterns in both AWS and Azure. The goal is to provide you with an end-to-end ecosystem experience, not just isolated tutorials.

Why Documentation Matters

In a fast-paced, ever-evolving DevOps landscape, good documentation is often the difference between chaos and clarity. Whether it is simplifying onboarding, reducing cognitive load, or making incident response smoother, documentation is a force multiplier.

This journey is also a way for me to reinforce my own learning, contribute to the community, and encourage others to build in public.

Who Am I?

I am a Site Reliability Engineer and DevOps Engineer with over 9 years of IT experience, 7+ of which have been deeply focused on DevOps practices. I have worked across startups and enterprise-level systems, leading automation efforts, designing cloud-native infrastructure, and building CI/CD ecosystems from scratch, while mentoring teams and promoting best practices along the way.

You can learn more about my background in my résumé summary.

What’s Next?

If you are on a similar journey, or just curious about how real-world DevOps, SRE, and MLOps projects work behind the scenes, stick around.

Let’s build, document, and grow together.