Course Overview
Moving from DevOps into MLOps means learning what changes when your deployment includes a trained model rather than application code alone. This course approaches that transition through a house-price prediction project. You follow the work from preparing data and experimenting with models to packaging an inference service, deploying it on Kubernetes, and adding automation and monitoring.
The emphasis is on connecting tools into a working delivery process. MLflow handles experiment tracking and model versions, FastAPI exposes predictions, and Docker packages the application. Later, Kubernetes, Seldon Core, and ArgoCD introduce infrastructure and delivery concerns. For someone who already understands containers and operational workflows, this provides a concrete setting for learning how machine learning fits into that familiar territory.
What You Will Learn
- Track model development: Use MLflow to record experiments and manage model versions as you work through the regression project.
- Prepare data for training: Explore data engineering, feature preparation, and experimentation using Jupyter notebooks.
- Expose predictions through an API: Package the trained model with FastAPI and containerize the service using Docker.
- Add a user-facing interface: Build a lightweight Streamlit application that interacts with the prediction backend.
- Automate integration work: Create GitHub Actions workflows and publish container images through Docker Hub.
- Run inference on Kubernetes: Use a local KIND cluster, configure services, and connect application components through service discovery.
- Explore model serving and observability: Work with Seldon Core, Prometheus, and Grafana.
- Apply GitOps to delivery: Use ArgoCD to manage deployment changes, while examining responsibilities across data science, ML engineering, and operations.
Course Structure
The course contains 97 lectures across 11 sections. Its project connects several stages of the machine learning lifecycle rather than treating each tool as an isolated tutorial.
The described progression starts with environment preparation, data work, and model experimentation. It then moves into API development, container packaging, a Streamlit interface, and automated integration. The later material addresses Kubernetes deployment, model serving, monitoring, and GitOps-based delivery.
Alongside the video lessons, the course includes 10 role-play activities, 10 articles, and 13 downloadable resources. These provide additional material beyond the recorded demonstrations.
Who Is This Course For?
The clearest fit is a DevOps engineer who wants to support machine learning workloads and needs a project that connects existing infrastructure skills with model development. Platform engineers, SREs, and cloud engineers working alongside ML teams are also part of the intended audience.
Developers moving toward ML engineering or data engineering may find the workflow useful, provided they can already work with Docker, Git, and basic Python. The stated prerequisites matter: this is not a starting point for learning programming or containers from scratch.
If your main goal is studying model mathematics or comparing many predictive algorithms, the course’s deployment-oriented scope is less aligned with that need. Its central exercise is one regression project, with substantial attention devoted to operating and delivering the resulting service.
Format & Time Commitment
The course offers 11 hours and 23 minutes of on-demand instruction, with access on mobile devices and TV. Closed captions are included, and the listing identifies English audio with automatically generated English and Spanish subtitles.
Treat the video runtime as viewing time, not the full project workload. Following the labs means configuring tools, running containers, and working through deployment steps yourself, so allow additional time for implementation and troubleshooting. The local lab requirement is a computer with at least 8 GB of RAM and Docker installed.
Pros and Cons
Pros
- One connected project: The house-price use case gives data preparation, experiment tracking, API development, and deployment a shared purpose.
- Relevant operational coverage: CI workflows, Kubernetes services, GitOps, and monitoring directly address the infrastructure side of ML delivery.
- More than model packaging: The syllabus extends beyond putting a model in a container to include serving infrastructure and observability.
Cons
- The entry requirements are meaningful: Learners without Docker, Git, or Python familiarity will face several learning curves at once.
- Breadth limits room for depth: Covering this many tools in roughly eleven and a half hours leaves limited space for extensive treatment of each platform.
- One project narrows the practice: A house-price regression workflow does not provide the same variety as working across several model types and deployment scenarios.
FAQ
Can I take this without a DevOps background?
The course expects basic DevOps and Docker knowledge, familiarity with Git and GitHub, and some Python exposure. CI/CD experience is helpful but not mandatory.
Do I need cloud infrastructure for the local Kubernetes exercises?
The course uses KIND for local Kubernetes deployment. Its stated machine requirement is at least 8 GB of RAM with Docker installed.
Does it include a certificate?
Yes, a completion certificate is included. The useful practical outcome is also the opportunity to build and explain the project’s training-to-deployment workflow.
Is this focused on LLMOps?
The advertised project predicts house prices. Its focus is conventional machine learning delivery and operations rather than an LLM application.
Explore the official Udemy course page if this project matches the infrastructure skills you want to practise next.
