About MLOps Engineering
MLOps is the craft of taking machine learning models out of notebooks and running them
reliably in production, and this course walks through it in the order most engineers
actually learn it. It starts with Python & ML Foundations and
Linux, Git & DVC, so you can write, version, and reproduce both
code and data.
Next comes Data Pipelines: ingesting, validating, and transforming data
with tools like Airflow and Spark. From there,
Experiment Tracking & Registry covers MLflow, Weights & Biases,
and model versioning, while Containers & Orchestration covers
Docker and Kubernetes.
Testing & CI/CD for ML covers data and model tests plus automated
training and deploy pipelines, and Model Serving & Deployment
covers REST and batch inference, FastAPI, KServe, and Triton, along with canary and
shadow rollouts.
Finally, Cloud & Infrastructure as Code covers AWS, GCP, Terraform,
and GPU workloads, ML Pipelines & Feature Stores covers
orchestrating end-to-end training with Kubeflow and Feast, and
Monitoring & Governance covers drift detection, observability, and
retraining: the skills that separate a model that works once from one that keeps
working.
Content
10 modules
01
Python & ML Foundations
Writing clean, testable Python and understanding the training, evaluation, and inference lifecycle of a machine learning model.
Pythonscikit-learnPyTorch
02
Linux, Git & DVC
Working comfortably in the shell, tracking code with Git, and versioning datasets and models with DVC so every result is reproducible.
BashGitDVC
03
Data Pipelines
Ingesting, validating, and transforming data in batch and streaming workflows, and scheduling it all reliably.
AirflowSparkGreat Expectations
04
Experiment Tracking & Registry
Logging parameters, metrics, and artifacts, comparing runs, and promoting models through a registry with clear versions and stages.
MLflowWeights & BiasesModel Registry
05
Containers & Orchestration
Packaging training and serving code into images, then running and scaling those workloads on a cluster.
DockerKubernetesHelm
06
Testing & CI/CD for ML
Unit tests for code, validation tests for data and models, and pipelines that retrain, evaluate, and ship automatically.
pytestGitHub ActionsContinuous Training
07
Model Serving & Deployment
Exposing models as real-time APIs or batch jobs, and rolling out new versions safely with canary and shadow deployments.
FastAPIKServeTriton
08
Cloud & Infrastructure as Code
Provisioning repeatable infrastructure on a major cloud, including storage, networking, and GPU compute for training and inference.
AWSTerraformSageMaker
09
ML Pipelines & Feature Stores
Orchestrating end-to-end training workflows and sharing consistent features between training and serving.
KubeflowFeastPipeline Orchestration
10
Monitoring & Governance
Watching latency, data drift, and model quality in production, triggering retraining, and keeping models auditable and compliant.
PrometheusEvidentlyDrift Detection
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior to mid-level MLOps role expects. Concretely,
you'll be able to:
Make ML reproducible: version code, data, and models with Git and DVC so any result can be rebuilt.
Build data pipelines: ingest, validate, and transform data on a schedule with Airflow and Spark.
Track and manage models: log experiments with MLflow and promote models through a registry.
Containerize and orchestrate: package workloads with Docker and run them on Kubernetes.
Automate delivery: set up CI/CD that tests data and models, then retrains and deploys automatically.
Serve models in production: ship real-time and batch inference, with safe rollouts and rollbacks.
Operate and monitor: provision cloud infrastructure as code, detect drift, and keep models healthy over time.