Sefism MLOps and LLMOps Roadmap

What this roadmap is

The MLOps and LLMOps roadmap is Sefism's path for developers and ML learners who can train a model or call an LLM but have never shipped one and kept it running. Tauseef Fayyaz built it around a full production loop: package the code, version the data, track the runs, orchestrate the pipeline, serve it, gate every release, monitor it, and decide with evidence when to retrain, re-prompt or fine-tune. It ends with a capstone that has to stay live for at least two weeks.

You choose which models you will run in production:

• Classic ML (the default): tabular and deep learning models, with saved pipelines, model signatures, feature stores, Evidently drift reports, ONNX export and GPU serving.
• LLM apps: prompts, evals and serving open models, with one module for every model call, versioned documents and indexes, prompt registries, eval gates, LoRA fine-tuning and vLLM.

Both focuses keep an LLMOps stage, because most ML teams now run at least one LLM feature, and both keep the classic stages, because the same discipline applies to prompts and models.

By the end you should be able to:

• Turn notebook code into a tested, locked and containerised Python project that trains or runs with one command.
• Version data, models and prompts so any prediction can be traced back to the exact code, data and configuration that produced it.
• Build an orchestrated pipeline that validates data, trains or evaluates, compares with the live version and promotes only on a quality gate.
• Serve a model or LLM feature behind an API with sensible latency, batching and cost, and release it with shadow, canary and rollback.
• Monitor service health, drift and output quality, trace LLM calls, and decide with evidence when to retrain, re-prompt or fine-tune.

Who it suits, and who should start elsewhere

It suits ML learners whose projects end in a notebook, data scientists who want their models used, and backend engineers moving into ML platform work. It assumes you can already build a model or an LLM feature.

If you cannot yet train and evaluate a model, start with the Machine learning roadmap. If you have not built an LLM feature, start with AI engineering. If containers, CI and cloud deployment are new, the DevOps and cloud roadmap gives you that ground first. And if agents in particular are your goal, see AI agents and MCP.

Prerequisites and realistic time

Comfortable Python, Git, and one model or LLM feature you have built and want to ship. Docker helps. Free notebook GPUs are enough for the fine-tuning stage. The roadmap adds up to about 265 hours across 10 stages:

• At about 5 hours a week (the default on the public page): roughly 53 weeks.
• At about 8 hours a week: roughly 38 weeks.
• At about 12 hours a week: roughly 28 weeks.

The first stage, from notebook to package, is a foundation stage that is skipped or shortened if you already structure projects this way.

A sample week for a student (10 to 12 hours). Two evenings on the stage's course module (MLOps Zoomcamp and Made With ML recur throughout), one evening on the checklist, and a weekend block extending the same project. Plan the capstone for a period when you can leave a service running for two weeks.

A sample week for someone working full time (5 hours). One weekday session reading, one on a small change to your project, and a three-hour weekend block. Pick a model you already have so that every hour goes into the operations, not the modelling.

Stage by stage

1. From notebook to package (20 hours, foundation)

The lifecycle and tool map, why notebook code breaks in production, project layout and pyproject.toml, lockfiles with uv, config and secrets outside the code, one command to train (classic) or one module for every model call (LLM), tests for ML code, and a container. Milestone: a src package with uv.lock, passing pytest tests and a Docker image that trains or runs the feature with one command.

2. Data and model versioning (15 hours)

Why Git alone is not enough, DVC next to Git, reproducible dvc.yaml stages, validating data before use, a frozen evaluation set, versioning documents, chunks and indexes (LLM), and lineage from a prediction back to its data. Milestone: data in a DVC remote, a pipeline that rebuilds with one command, validation that fails on bad data, and a frozen evaluation set.

3. Experiment tracking and the model registry (15 hours)

What to log, MLflow tracking and a shared server, comparing runs honestly, the registry and aliases, signatures (classic) or prompts in a registry (LLM), and reproducing a run end to end. Milestone: at least ten compared runs, a registered model or prompt with a champion alias, and one run rebuilt from its records alone.

4. Pipelines and orchestration (25 hours)

Splitting work into steps, DAGs and choosing an orchestrator, a first flow, idempotent tasks, schedules, triggers and backfills, and an optional feature store (classic) or an indexing pipeline for RAG (LLM). Milestone: a scheduled pipeline that validates, retrains or re-indexes, evaluates against the current version and registers only when it passes. Prefect is suggested as the quickest start; Airflow is what many data teams already run.

5. Serving: batch, real time and GPUs (30 hours)

Batch or online serving, a validated model API, serving frameworks, packaging and deploying, latency percentiles and load testing, and batching, GPUs and scaling. Milestone: your model behind a validated API on a managed platform, with a load test report of p50, p95 and p99 latency, throughput and the point where errors start.

6. CI/CD for ML (25 hours)

MLOps maturity levels, a CI pipeline for ML code, quality gates and champion versus challenger, eval gates for prompts (LLM), shadow, canary and A/B releases, rollback in minutes, and continuous delivery of models. Milestone: a GitHub Actions workflow with a quality gate and a tested rollback. The rule: gates before automation.

7. Monitoring, drift and retraining (25 hours)

Monitoring the service, logging predictions, types of drift, statistical detection, drift reports with Evidently, delayed labels and proxies, when to retrain, and alerts people act on. Milestone: a Grafana dashboard, logged predictions joined to outcomes, a scheduled drift report and a retraining trigger that goes through your gate.

8. LLMOps: prompts, evals, tracing, caching, cost (35 hours)

Prompts as code, eval sets, metrics and LLM judges, tracing every call, caching, token budgets, gateways, fallbacks and rate limits, prompt injection and guardrails, and online evals. Milestone: an LLM feature with versioned prompts, a 50-case eval set with a validated judge in CI, tracing in Langfuse or Phoenix, a cache, a cost-per-request report and a documented injection guardrail. "Evals first, everything else second."

9. Fine-tuning and serving open models (35 hours)

On LLM apps: prompting, RAG or fine-tuning, LoRA and QLoRA, preparing data, running a fine-tune, quantisation, serving with vLLM and GPU cost arithmetic. On classic ML the stage becomes Fine-tuning pretrained models and GPU serving, with ONNX export and GPU serving. Milestone: a tuned, exported or quantised model served with a load test and compared against the baseline on quality, latency and cost.

10. Capstone: a monitored model in production (40 hours)

Scoping, architecture decisions, building the loop end to end, a model card, a runbook and incident drill, a cost and performance report, and the write-up. Milestone: a live model or LLM feature running for at least two weeks with every earlier piece in place and a README with a model card.

Projects to build alongside

On classic ML: Pharmacy Demand Forecasting Service with Drift Monitoring and Automated Retraining and Predictive Maintenance from Machine Vibration Data. On LLM apps: Fine-Tuned Small Open Model with Eval-Gated Releases, Evaluation Harness for Language Model Applications and LLM Gateway with Semantic Cache and Model Routing.

How it works on Sefism

Everything on Sefism is free to learn from. The public roadmap page shows all ten stages with their summaries and length and opens the first two in full. Adding the roadmap to your dashboard opens every stage, tracks your progress as you tick stages off and gives you the companion cheat sheet. Your first roadmap is free to add; after that, adding a roadmap uses credits, and new members get starting credits when they join.

Each topic in the dashboard has an explanation, a check question and an AI prompt that quizzes you, and each stage has Tauseef's advice, a checklist and curated resources. You can ask Tauseef a question about any stage.

The companion cheat sheet

The MLOps and LLMOps cheat sheet revises all ten stages with an edition for each focus. For the tracking stage, the standalone MLflow cheat sheet goes deeper into the tool's API.

Related on Sefism

• How to become an MLOps engineer
• How to deploy a machine learning model from a notebook
• MLOps Zoomcamp, Made With ML and DVC Documentation, recurring stage resources.
• Related roadmaps: Data engineering and Platform engineering and SRE.

What you can do afterwards

Show a model or LLM feature that has run in production for weeks, with a gate, a dashboard, a rollback you have practised and a cost report. As the capstone advice puts it, a simple model that is deployed, monitored and rolled back once is worth more than a clever one on a laptop. That is a strong basis for MLOps, ML platform and ML engineering applications.

FAQ

How long does it take to learn MLOps? The roadmap estimates about 265 hours: around 28 weeks at 12 hours a week or 53 weeks at 5 hours a week.

What is the difference between MLOps and LLMOps? The same discipline applied to different things. MLOps versions data and models and watches drift; LLMOps versions prompts and indexes, runs eval sets and judges, traces calls and controls token cost. The roadmap teaches both.

Do I need a GPU to learn MLOps? No. Free notebook GPUs are enough to learn LoRA on small models, and the roadmap asks you to do the GPU arithmetic before renting anything.

Should I fine-tune or use RAG? Try prompting and RAG first. The fine-tuning stage starts by asking you to write down, with eval evidence, why the task needs it.