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MLOps Mastery: From Model Development to Scalable Production on AWS & Kubernetes
USD $249.00
The MLOps Mastery Workshop is a hands–on training program designed to develop production-ready machine learning skills. Participants learn how to build, deploy, automate, and monitor ML systems using modern MLOps tools such as Git, Docker, Kubernetes, and AWS. The workshop provides practical experience with automated pipelines and real-world deployment workflows, preparing learners for industry-focused ML Engineering and MLOps roles.
About the Course
This workshop addresses the full lifecycle of machine learning in production, from version control to automated pipelines, containerized deployment, CI/CD, orchestration, and system observability. While data science often emphasizes model accuracy, real-world impact depends on operational stability, reproducibility, and scalability.
Participants gain hands-on experience with modern MLOps practices, including automated workflows, experiment tracking, container orchestration with Kubernetes, cloud-native deployment on AWS, and monitoring live ML systems. The course bridges the persistent gap between machine learning experimentation and industrial application.
“By completing this workshop, learners can confidently design, implement, and maintain production-ready ML systems that are reliable, scalable, and aligned with industry standards.”
Why This Topic Matters
Deploying ML models is significantly more complex than building them. Organizations struggle with:
- Reproducibility: Ensuring experiments can be rerun and audited.
- Scalability: Supporting millions of requests while maintaining model performance.
- Automation: Reducing human error in CI/CD pipelines for model deployment.
- Observability: Monitoring drift, latency, and system health in production.
As cloud adoption and AI workloads expand, demand for professionals who can operationalize machine learning is growing rapidly. Knowledge of MLOps tools, infrastructure, and deployment strategies is no longer optional—it’s the baseline for production ML roles.
What Participants Will Learn
• Design reproducible ML workflows using DVC and modular pipelines
• Implement experiment tracking and manage model versions with MLflow
• Containerize ML applications with Docker and deploy APIs via FastAPI
• Automate deployment pipelines using GitHub Actions and CI/CD strategies
• Orchestrate scalable workloads with Kubernetes on AWS EKS
• Monitor production ML systems using Prometheus and Grafana
• Develop an end-to-end industry-grade ML system, from ingestion to live deployment
Course Structure / Table of Contents
Module 1 — Version Control & Collaboration
- Git workflows for ML projects
- Collaborative coding with GitHub
- Repository management automation
Module 2 — Data & Pipeline Versioning
- Building reproducible pipelines with DVC
- Integrating remote storage (Amazon S3)
- Parameterized and modular ML workflows
Module 3 — Experiment Tracking & Model Management
- End-to-end tracking using MLflow
- Model registry versioning
- Integration with collaborative platforms (Dagshub)
Module 4 — Containerization & ML Deployment
- Docker fundamentals and optimization
- Building ML APIs with FastAPI
- Publishing images to Docker Hub & Amazon ECR
Module 5 — Real Industry Project: End-to-End MLOps System
- Vehicle insurance claim prediction system
- Automated pipelines, model tracking, containerized deployment
- CI/CD integration and Kubernetes production rollout
- [Visual Note: Insert workflow diagram for course progression]
Tools, Techniques, or Platforms Covered
Git, GitHub
DVC, Amazon S3
MLflow, Dagshub
Docker, FastAPI, AWS ECR, EC2
Kubernetes, AWS EKS
GitHub Actions
Prometheus, Grafana
DVC, Amazon S3
MLflow, Dagshub
Docker, FastAPI, AWS ECR, EC2
Kubernetes, AWS EKS
GitHub Actions
Prometheus, Grafana
Real-World Applications
Production ML pipelines in finance, healthcare, and insurance; cloud ML system deployment and scaling; experiment tracking and model versioning for collaborative teams; monitoring AI systems for drift, latency, and reliability; portfolio-level project experience demonstrating industry-ready skills.
Who Should Attend
- Data Scientists: Transition from experimentation to production ML systems
- Machine Learning Engineers: Expand deployment and scaling expertise
- Software/DevOps Engineers: Integrate AI into cloud-native systems
- Cloud Professionals: Operate ML workloads on AWS & Kubernetes
- Students/Professionals: Aspiring MLOps engineers or ML-focused career movers
Prerequisites or Recommended Background: Basic machine learning knowledge and Python programming. Familiarity with data structures, APIs, and cloud concepts recommended. No prior experience with Kubernetes or CI/CD required; workshop guides step-by-step.
Why This Course Stands Out
End-to-End Production Focus, Industry-Grade Tool Exposure, Applied Learning with real projects, Balanced Curriculum combining theory and practice, and Expert-Led Guidance from ML engineering professionals.
| Category | E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures |
|---|
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