MLOps Engineering
Notebooks don't make money. Take models from experiment to production — versioned, monitored, automatically retrained, and running at a cost the business can justify. The infrastructure and reliability track for both classical ML and LLM workloads, built for fresh IT graduates and working professionals alike.
Our other AI courses teach you to build AI. MLOps Engineering teaches you to operate it — the person who makes models and LLM workloads run, scale, stay accurate and not blow the budget. This is a live, infrastructure-first program where every module ends with something deployed: a reproducible pipeline, an automated retraining loop, a canary rollout, a monitoring stack that catches a model degrading quietly.
It is designed for two audiences at once. If you are a fresh IT graduate, it is an infrastructure-side entry into AI that stands on durable DevOps and platform fundamentals. If you are a working professional — DevOps, cloud, backend, data engineering, or a data scientist tired of handing notebooks over the wall — your existing experience transfers directly, and this course turns it into ML platform ownership.
The program runs 14 weeks (~110 hours), live online, and you graduate with a portfolio of deployed systems — closing with a capstone where you design, build and defend one end-to-end production ML platform.
Is This the Right Course for Me?
- Prompt Engineering — reliable AI outputs; the widest audience and the lowest prerequisite.
- Generative AI & LLM Engineering — the broad builder foundation: models, RAG, fine-tuning and LLMOps basics.
- AI Agent Development — the specialist deep-dive: autonomous, tool-using, multi-agent systems.
- MLOps Engineering (this course) — the infrastructure and reliability track: you make everything above run, scale, stay accurate and stay affordable.
Course at a Glance
- Duration: 14 weeks | ~110 hours | live + hands-on
- Format: Live online · infrastructure-first · portfolio of deployed systems
- Prerequisite: Working Python, comfort with the command line and Git. No deep ML theory required — you'll operate models, not derive them.
- Curriculum: 11 modules, from the production gap to cloud, cost and governance
- Portfolio: 7 deployed, reproducible systems plus a defended capstone
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Reproducibility as a discipline: MLflow, DVC, model registry, exact rebuilds months later
- Automated retraining triggered by drift signal, not by calendar
- Kubernetes taught after Docker is solid — including when not to reach for it
- Monitoring for models that degrade quietly: drift, canary and shadow deployments
- Deep LLMOps: eval pipelines in CI, tracing, vLLM serving, token-level cost attribution
- FinOps for GPU spend, IaC with Terraform, governance and audit readiness
Hands-on Projects & Portfolio
MLflow tracking → model registry → containerized service → deployed endpoint, reproducible from a clean clone.
Automated retraining pipeline in Airflow or Kubeflow, triggered by an Evidently drift alert.
KServe traffic splitting, compared on latency and accuracy.
Prometheus + Grafana dashboards with alerting on drift and prediction distribution shift.
A pipeline that tests, validates and gates a model before it can reach production.
vLLM serving with an eval regression suite and per-request cost tracking.
Infrastructure-as-code teardown and rebuild of the whole platform from Terraform.
Tools & Stack
Python Git Docker Kubernetes MLflow Weights & Biases DVC Apache Airflow Kubeflow KServe BentoML FastAPI vLLM Evidently Prometheus Grafana LangSmith Langfuse GitHub Actions Argo CD Terraform AWS SageMaker GCP Vertex AI Azure ML
Career Outcomes
MLOps is one of the more durable AI roles because it sits on DevOps and platform fundamentals that don't churn every six months — and enterprises now need classical ML operations and LLMOps practices side by side. The natural on-ramps are DevOps, cloud, backend and data engineering: existing infrastructure experience transfers directly. This course targets:
- MLOps Engineer
- ML Platform Engineer
- ML Engineer
- AI Infrastructure Engineer
- LLMOps Engineer
- Data Engineer (ML focus)
- DevOps Engineer moving into ML
- Site Reliability Engineer for AI systems
Who This Course Is For
- Final-year students and fresh IT graduates who want an infrastructure-side entry into AI
- DevOps, cloud and platform engineers adding ML workloads to what they already operate
- Data scientists tired of handing notebooks over the wall and wanting to own production
- Backend and data engineers moving into ML platform work
- SREs and architects responsible for AI systems' uptime, cost and governance
Material Includes
- Class recordings
- Lab guides & infrastructure starter repos
- Runbooks & incident-response templates
- Certificate
Prerequisites
- Working Python
- Comfort with the command line and Git
- No deep ML theory — you'll operate models, not derive them
Estimated Time: 8 Hours
You ship:- A written post-mortem analysis of a real production ML incident — what failed, who should have owned it, and the maturity gaps that let it happen
- Why most models never ship; the ML lifecycle end to end
- MLOps maturity levels; who owns what across data science / engineering / ops
- Reading a real production incident post-mortem on day one
Estimated Time: 8 Hours
You ship:- A production-structured ML repository — tested, environment-pinned, configuration-managed — that becomes the base for every later module
- Git workflows for ML repos; project structure; dependency and environment management
- Testing ML code; configuration management
- The Linux/networking basics the rest of the stack assumes
Estimated Time: 10 Hours
You ship:- A tracked, versioned training workflow — MLflow runs, registered models, DVC-versioned data — where a result from three months ago can be rebuilt exactly
- MLflow and Weights & Biases; run tracking, parameters and metrics; artifact storage
- The model registry; model lineage
- DVC for data and pipeline versioning
Estimated Time: 8 Hours
You ship:- A validated data pipeline with schema-enforced quality gates and a skew check that catches training-serving drift before it reaches customers
- Data validation and quality gates; schema enforcement
- Feature stores and online/offline consistency
- Training-serving skew and how to detect it
Estimated Time: 12 Hours
You ship:- A containerized model service — FastAPI or BentoML — with warm-up, request batching, and a measured latency budget, plus a defended GPU-vs-CPU serving decision
- Docker done properly for ML images
- FastAPI and BentoML services; real-time vs batch vs streaming inference
- Model loading and warm-up; request batching; latency budgets; GPU vs CPU serving decisions
Estimated Time: 10 Hours
You ship:- An automated retraining pipeline in Airflow or Kubeflow — idempotent, retry-safe, parameterized — triggered by signal rather than by calendar
- Apache Airflow and Kubeflow Pipelines; DAG design
- Scheduling and backfills; retries and idempotency; parameterized training runs
- Automated retraining triggered by signal
Estimated Time: 10 Hours
You ship:- A CI/CD pipeline that tests, validates and gates a model before production — with GitOps rollback so a bad model reverts with a git revert
- GitHub Actions / GitLab CI; why ML pipelines need continuous training on top of CI/CD
- Automated model validation gates; model approval workflows
- GitOps with Argo CD
Estimated Time: 12 Hours
You ship:- A KServe deployment serving a predictive model and an LLM — autoscaled, quota-bound, scale-to-zero — plus a one-page "do we even need Kubernetes?" decision brief
- Introduced only after Docker is solid: deployments, services, autoscaling
- KServe for serving predictive models and LLMs; scale-to-zero; resource quotas and GPU scheduling
- An honest module on when not to reach for Kubernetes
Estimated Time: 12 Hours
You ship:- A full monitoring stack — Prometheus + Grafana + Evidently — with drift alerts, a canary rollout with traffic splitting, and an incident runbook for models that degrade quietly
- Prometheus and Grafana for system health; Evidently for data drift and concept drift
- Prediction distribution monitoring; alerting thresholds; feedback loops
- Shadow deployments; canary and A/B rollouts with traffic splitting; incident response for models that degrade quietly instead of failing loudly
Estimated Time: 10 Hours
You ship:- A production LLM serving stack — vLLM, caching, model routing — with an eval regression suite in CI, tracing, token-level cost attribution and guardrail monitoring
- Where operating LLMs differs from classical ML: prompt and context versioning; eval pipelines in CI
- Tracing with LangSmith / Langfuse; vLLM and inference optimization; caching; model routing
- Token-level cost attribution; guardrail monitoring
Estimated Time: 10 Hours
You ship:- One end-to-end production ML platform — built as code with Terraform, torn down and rebuilt on demand — and defended in a capstone review
- SageMaker / Vertex AI / Azure ML managed paths vs self-hosted; infrastructure as code with Terraform
- FinOps for GPU spend; security, access control, audit trails
- Model documentation and regulatory readiness; capstone design, build and defense
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- Email: Send your inquiries to hello@progmatrix.com, and one of our representatives will get back to you as soon as possible.
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