What You’ll Learn: MLOps Fundamentals
You’ll go from understanding basic ML models to building and maintaining end-to-end production systems that are robust, scalable, and reliable.
ML Pipeline Architecture
Design and implement ETL pipelines for data processing and model training.
Experiment Tracking & Model Registry
Use tools like MLflow to log experiments, register models, and manage versions.
Docker & Containerization
Package your models and dependencies into portable Docker containers.
Deployment & Orchestration
Deploy models to cloud platforms or Kubernetes clusters for scalable inference.
Who Is This Course For?
Ideal for ML engineers and data scientists looking to transition from model prototyping to production deployment and operations.
- ML engineers ready to specialize in MLOps
- Data scientists wanting to deploy models at scale
- Developers building AI-powered applications
Hands-On Projects
End-to-End ML Pipeline
Build a complete pipeline using a framework like KFP or Airflow to automate data prep, training, and evaluation.
CI/CD for Model Retraining
Set up a GitHub Actions workflow to automatically retrain and validate models on new data.
Capstone
Production Model API
Containerize a trained model with Docker, deploy it on Kubernetes, and serve predictions via a REST API.
3-Week MLOps Syllabus
~36 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: ML Lifecycle & Core Tools
- Introduction to MLOps principles and challenges
- Model lifecycle stages (development, validation, deployment, monitoring)
- Experiment tracking with MLflow (tracking server, model registry)
- Basic Docker usage for ML environments
Week 2: Pipelines & CI/CD
- Building ML pipelines with KFP or Airflow
- Parameterizing pipelines for reusability
- Implementing CI/CD for ML using Git and GitHub Actions
- Automated testing and model validation steps
Week 3: Deployment & Monitoring
- Model serving strategies (batch vs. real-time)
- Containerizing models with Docker
- Orchestrating deployments with Kubernetes (basic concepts)
- Model monitoring, logging, and drift detection
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Basic familiarity with Docker concepts is helpful, but not required. We will cover the essentials. Kubernetes knowledge is beneficial but not mandatory. A strong understanding of Python and machine learning is necessary.
Yes! You will learn to containerize models with Docker and deploy them to cloud platforms (AWS, GCP, Azure) or local Kubernetes clusters. We will use tools like KFServing or Seldon for orchestration.
