MLOps Certification Course Online: Build, Scale and Operate Reliable ML Systems

DevOps manages software release cycles. MLOps handles the additional complexity of ML systems — data drift, model decay, training-serving skew, and the need for reproducible experiments. The infrastructure and lifecycle discipline is different because ML systems depend on data, models, and code together, not just code.

Both. Beginners start with the taught foundations — Python, Git, Docker and Kubernetes are built from scratch inside the programme. Data scientists add reproducibility, release discipline and monitoring; ML engineers add lifecycle automation; DevOps engineers add the ML-specific pipelines, model registry and drift handling.

5 months, live online, instructor-led. Weekday and weekend batches are available. The programme follows a Learn → Build → Review → Break → Debug → Operate loop, moving from data foundations to a full production MLOps system with an AWS SageMaker capstone.

None. No prior programming or MLOps experience is required to join. Python, Git, Docker and Kubernetes foundations are taught within the programme, starting with the foundations that feed into the data pipelines, training and release work.

Pandera and Great Expectations for validation, DVC for versioning, Ray Train for distributed training, MLflow for tracking and registry, Docker/Kubernetes/Helm for deployment, FastAPI and TorchServe for serving, Prometheus/Grafana/Evidently for monitoring and drift detection, and AWS SageMaker for managed cloud deployment.

Five connected production builds plus a capstone: event-driven data orchestration, reproducible versioning, distributed training with experiment tracking, model registry with CI/CD release, drift detection with controlled retraining, and an AWS SageMaker deployment capstone. Each project connects to the next — not isolated tool demos. See the production projects behind the curriculum at /mlops/projects and inspect the end-to-end implementation at /mlops/projects/end-to-end-mlops-project.

MLflow Tracking is used for experiment tracking — logging parameters, metrics, and artifacts across training runs. MLflow Model Registry is used for versioning, promoting, and managing model lifecycle through governed registry gates. You learn to compare runs, select candidates, and manage the transition from staging to production.

You learn production patterns for ML on Kubernetes — Helm for package management, deployment strategies for inference APIs, and resource management for training and serving workloads. The focus is on using Kubernetes as part of the release pipeline, not as a standalone DevOps topic.

Yes. You implement monitoring with Prometheus and Grafana, drift detection with Evidently, and a controlled retraining pipeline that validates challenger models before promotion. The goal is to detect drift, alert on it, and respond with a validated retraining decision — not just observe dashboards.

Yes. You receive an MLOps Course Completion Certificate after completing all assignment-based skill alignment requirements and the capstone project. The certificate is backed by reviewed, production-ready project work — repositories, pipelines, tests, and runbooks.

Career support includes resume review, GitHub and project presentation review, mock interviews, interview preparation, job-search guidance, and referral connections where available. We do not guarantee placement — the focus is on preparing you with real production skills and interview readiness.

MLOps covers the production lifecycle for traditional ML systems — data pipelines, training, registry, serving, monitoring, and retraining. LLMOps covers the production lifecycle for language-model systems — LLM fine-tuning, RAG, prompt versioning, vector databases, and LLM serving. This course teaches the core MLOps foundation; both rely on the same CI/CD and monitoring principles.