ML Ops Fundamentals Course | MLOps Specialization

Clarify the distinction between ml algorithms and ml models, showing how algorithms define the learning process. Describe how a trained model is deployed, monitored, retrained, and versioned in production.

Explore the random forest classifier, an ensemble of decision trees that uses majority voting to classify data, handles mixed data types, and highlights feature importance on the Titanic dataset.

Set up a lightweight Python environment, install essential libraries, and load the Titanic train data to train a random forest classifier with scikit-learn.

Ensure training data is reliable, safe, and consistent by applying data quality checks, validation, monitoring for data drift, and lineage, addressing bias, privacy, and regulatory concerns to sustain model usefulness.

Learn how to train a random forest on the Titanic dataset and manage training scripts for reliable production pipelines, including data loading, preprocessing, feature selection, model artifacts, and reproducibility.

Explore the MLOps model lifecycle from an MLOps perspective, covering data collection and preparation, training, validation, deployment, monitoring, retraining, and governance.

Learn how common mlops tools support the full ml model lifecycle, from data versioning with DVC and data lineage to training, validation, deployment, and monitoring.

Deep dive into ML models explains why mastering data, model retraining, and production behavior matters in MLOps, guiding you to related courses to build production-ready skills.

Explore the three core ci/cd practices—continuous integration, continuous delivery, and continuous deployment—and how automated tests, code quality checks like SonarQube, and reproducible artifacts enable safe production releases.

Implement core ci/cd principles by dividing stages for specific responsibilities, enabling fast feedback and early issue fixes, while integrating security and secrets management in a unified release workflow.

MLOps extends DevOps by integrating data versioning, dataset quality checks, and retraining with changing data. Track experiments, manage model artifacts, and deploy a model-serving API with preprocessing and feature extraction.

Discover common MLOps CI/CD tools for automating pipeline stages in real-world projects, including Jenkins, GitHub Actions, GitLab CI/CD, and CircleCI for code, testing, packaging, and model delivery.

Create a minimal CI/CD pipeline using GitHub Actions to run a random forest training on the Titanic dataset, installing dependencies and logging training outputs in the Actions UI.

Design, test, secure, and scale CI/CD pipelines that support real ml workflows across the full model lifecycle. Automate training, testing, packaging, and deployment using GitHub Actions, Docker, and model registries.

Understand how docker containers power most ml projects, how compute environments vary from vms to bare metal, and how kubernetes orchestrates workloads across cloud, on-prem, and hybrid deployments.

Package the titanic random forest model in a docker container, train inside the image, print the accuracy, and run with a single docker command for scalable ml deployment.

Discover how infrastructure underpins ML workflows and how teams provision, secure, and automate compute, storage, and networks using infrastructure as code with Terraform, Pulumi, or OpenTofu.

Deploy a docker image that trains a random forest on the Titanic dataset into Kubernetes with Minikube. Run the training as a Kubernetes Job and inspect logs for accuracy.

Explore how cloud infrastructure powers modern mlops. AWS, Azure, and GCP drive most workloads, offering scalable compute and services for training, deployment, and monitoring.

Build secure, scalable, and cost-efficient mlops infrastructure using containers, Kubernetes, and infrastructure as code, while mastering troubleshooting, CI/CD integration, and decisions based on business, data, and model needs.

Explore observability in ml systems, extending beyond monitoring to reveal why models fail by tracking data drift, data quality, model performance, and prediction distribution across data, infrastructure, and business metrics.

Explore observability for a random forest model on the Titanic dataset, exposing metrics with Prometheus, deploying on Kubernetes, and visualizing with Grafana.

Explore why observability is essential in ml ops, learn how to detect data shifts and failing models, and build and maintain observability in real production systems.

Explore how security and governance shape MLOps, covering access control, data encryption, logging, privacy, and regulatory standards like GDPR, HIPAA, and SOC 2 to safely deploy ML in regulated environments.

Learn how FinOps aligns engineering decisions with financial visibility in MLOps, tracking and reducing cloud costs across data preprocessing, retraining, idle resources, and GPU usage in ML pipelines.

Explore key roles in real ai/ml teams and how MLOps engineers, data scientists, ML engineers, data engineers, and product managers collaborate to build, deploy, and manage production ML systems.

Master infrastructure setup and automation, build and deploy ml pipelines with ci/cd, track model and data versions, monitor systems, and collaborate across teams to deliver reliable, scalable ml solutions.

Navigate a practical MLOps engineer roadmap that builds the right mindset and structure to support real machine learning systems, from Python and Git to cloud and containers.

Explore MLOps job descriptions, detailing must-have skills in machine learning algorithms, Docker and Kubernetes, CI/CD, Terraform, Helm and Kustomize, monitoring, and model orchestration and pipelines on AWS, GCP, and Azure.

Explore MLOps salaries for engineers worldwide, from the US 190k–280k range to the UK and Europe, India, the Middle East, and Asia. See how location and experience drive pay.

Finish the MLOps fundamentals course and begin the MLOps specialization, gaining real projects, a portfolio, and early access via the newsletter.