Hi Inner Circle!

Welcome to this week's edition.

DevOps, MLOps, AIOps, and LLMOps often get used interchangeably ~ but they solve very different problems.

Let’s unpack what each one focuses on and how the roles are evolving.

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DevOps, MLOps, AIOps, and LLMOps often get used interchangeably, but they actually solve very different problems in modern engineering systems. The easiest way to think about them is that:

DevOps

DevOps is the foundation of modern software delivery.

It focuses on shipping code reliably using automation, CI/CD pipelines, infrastructure as code, and strong observability.

Typical workflows involve

- planning backlogs,

- managing code through Git,

- running CI builds,

- automating tests,

- releasing via CD pipelines,

- and monitoring systems through logs and metrics.

Most people transition into DevOps from software engineering, Linux/system administration, or cloud engineering. The core skills to study include Linux fundamentals, networking basics, containers (Docker), Kubernetes, CI/CD tools, Terraform, and observability stacks.

Common roles include DevOps Engineer, Platform Engineer, Infrastructure Engineer, and SRE. In many ways, DevOps remains the operational backbone that everything else in modern engineering builds on.

Check out this project to dive deeper.

MLOps

MLOps exists because machine learning systems behave differently from traditional software.

Instead of just deploying code, teams must: manage datasets, feature pipelines, experiments, and retraining cycles.

A typical MLOps lifecycle includes:

- defining the ML problem - what is this model going to do?

- collecting and versioning data,

- performing feature engineering, training models,

- deploying them to serve users, and monitoring for drift or performance degradation.

Engineers usually transition into MLOps from data science or DevOps backgrounds. Data scientists learn production systems, while DevOps engineers learn ML workflows.

Key areas to study include ML fundamentals, feature stores, experiment tracking, ML pipelines, and platforms like MLflow, Kubeflow, or Vertex AI.

Typical roles include MLOps Engineer, ML Engineer, and ML Platform Engineer.

Explore this project to dive deeper (free).

Or follow this course for a structured learning path (paid)

AIOps

AIOps applies machine learning to operate large-scale IT systems. Modern infrastructure generates massive volumes of telemetry like logs, metrics, alerts, and traces ~ and AIOps platforms use ML to analyze this data, detect anomalies, correlate incidents, and trigger automated remediation.

Instead of engineers manually analyzing alerts, AIOps systems can:

- identify abnormal behavior,

- connect related signals,

- and initiate actions like rollback or scaling adjustments.

Most professionals moving into AIOps come from SRE, DevOps, or observability engineering roles.

Important areas to study include observability platforms, anomaly detection, LLM Fundamentals, time-series analysis, and monitoring ecosystems like Datadog, Splunk, or Elastic.

Typical roles include AIOps Engineer, Observability Engineer, and Reliability Platform Engineer.

If you’d like to dive deeper, this project is a great place to start.

LLMOps

LLMOps is the newest (not so new though) operational discipline, emerging with large language models and generative AI systems. Unlike traditional ML pipelines, LLM systems rely heavily on prompts, retrieval pipelines, vector databases, guardrails, and evaluation frameworks.

The lifecycle usually includes defining the AI use case:

- curating knowledge sources (external & internal),

- designing prompt templates,

- selecting models,

- implementing guardrails and evaluations,

- deploying inference systems,

- and monitoring hallucinations or latency.

Engineers often transition into LLMOps from MLOps, DevOps, or AI engineering roles.

Key topics to study include transformers, embeddings, vector databases, RAG architectures, prompt engineering, and GPU inference infrastructure.

Common roles include AI Engineer, Generative AI Engineer, and LLM Platform Engineer.

This video walks you through the full project, along with the key concepts behind it. Also here’s the code repository that you can review.

That’s it for this week.

Pick one concept, practice it, and extend it further.

Keep showing up, keep upskilling.

See you in the next one.

-V