MLOps Mastery: From Model Development to Scalable Production on AWS & Kubernetes

Introduction to the Workshop

The MLOps Mastery Workshop is a comprehensive, hands-on training program designed to transform machine learning practitioners into production-ready ML Engineers. While building machine learning models is important, real-world success depends on deploying, monitoring, scaling, and maintaining models efficiently in production environments.

This workshop bridges the gap between Data Science and DevOps by introducing modern MLOps practices, automation strategies, and cloud-native deployment techniques. Participants will learn how to design, deploy, and manage complete machine learning systems using industry-standard tools such as Git, Docker, Kubernetes, MLflow, and AWS services.

Throughout the workshop, learners will gain practical experience in building automated ML pipelines, tracking experiments, deploying containerized applications, and monitoring live machine learning systems—replicating real-world industry workflows followed by modern AI teams.

Workshop Objectives

• Understand the complete lifecycle of production machine learning systems.
• Learn version control and collaboration workflows used in ML teams.
• Build reproducible and automated machine learning pipelines.
• Implement experiment tracking and model registry management.
• Deploy scalable ML applications using containers and cloud platforms.
• Automate CI/CD workflows for machine learning projects.
• Monitor deployed ML systems for performance, reliability, and model drift.
• Prepare participants for real-world MLOps and ML Engineering roles.

What Will You Learn (Modules)

Module 1: Version Control & Collaboration

• Git workflows and branching strategies for machine learning projects.
• Collaborative development using GitHub.
• Repository management and automation best practices.

Module 2: Data & Pipeline Versioning

• Building reproducible ML pipelines using DVC.
• Integration with remote storage platforms such as Amazon S3.
• Creating modular and parameterized machine learning workflows.

Module 3: Experiment Tracking & Model Management

• End-to-end experiment tracking using MLflow.
• Managing multiple model versions through model registries.
• Integration with collaborative platforms like Dagshub.

Module 4: Containerization & ML Deployment

• Docker fundamentals and container optimization techniques.
• Building and deploying ML APIs using FastAPI.
• Publishing container images to Docker Hub and Amazon Elastic Container Registry.

Module 5: CI/CD for Machine Learning

• Automating ML workflows using GitHub Actions.
• Model validation and performance testing automation.
• Automated deployment on Amazon EC2 environments.

Module 6: Kubernetes for ML Workloads

• Container orchestration concepts using Kubernetes.
• Managing Deployments, Services, Ingress, and Horizontal Pod Autoscaling.
• Scaling machine learning applications using Amazon EKS.

Module 7: Monitoring & Observability

• Collecting infrastructure and application metrics using Prometheus.
• Building monitoring dashboards with Grafana.
• Tracking latency, throughput, model drift, and system performance.

Module 8: Real Industry Project – End-to-End MLOps System

Participants will develop a Vehicle Insurance Claim Prediction System including:

• Data ingestion from MongoDB.
• Automated pipelines using DVC.
• Experiment tracking with MLflow.
• Dockerized FastAPI deployment.
• CI/CD automation workflows.
• Kubernetes-based production deployment.
• Complete monitoring and observability integration.

Final Project

In the final project, participants will design and deploy a complete production-grade machine learning system. This includes implementing automated pipelines, experiment tracking, container orchestration, deployment automation, and monitoring infrastructure aligned with real industry practices.

Who Should Attend This Workshop?

• Data Scientists: Professionals looking to transition into production ML systems.
• Machine Learning Engineers: Individuals seeking advanced deployment and scaling expertise.
• Software Engineers: Developers moving toward AI and MLOps careers.
• DevOps Engineers: Engineers interested in machine learning infrastructure.
• Cloud Engineers: Professionals working with AI-enabled cloud systems.
• Students & Professionals: Learners aiming for industry-ready ML careers.

Career Opportunities After This Workshop

• MLOps Engineer
• Machine Learning Engineer
• AI Platform Engineer
• DevOps Engineer (ML Focus)
• Cloud ML Architect
• Production AI Engineer

Why Learn With Nanoschool?

At Nanoschool, we focus on practical and industry-oriented learning designed to prepare learners for modern AI and cloud-driven environments.

• Industry-Focused Curriculum: Learn tools actively used by leading AI organizations.
• Hands-On Training: Build real production-ready machine learning systems.
• Real-World Projects: Gain portfolio-level project experience.
• Expert Guidance: Learn from professionals experienced in ML engineering and cloud deployment.
• Career-Oriented Learning: Skills aligned with current MLOps job market demands.

Key Outcomes of the Workshop

• Build and manage end-to-end machine learning pipelines.
• Deploy scalable ML applications using Docker and Kubernetes.
• Implement CI/CD automation for ML workflows.
• Track experiments and manage model lifecycle efficiently.
• Monitor production ML systems for reliability and performance.
• Gain practical experience with real-world MLOps architecture.

FAQs

• What is MLOps?
  MLOps combines Machine Learning, DevOps, and cloud practices to automate, deploy, and manage machine learning models in production environments.
• Do I need prior machine learning experience?
  Basic knowledge of machine learning and Python is recommended, and the workshop guides participants step-by-step through production concepts.
• Will this workshop include hands-on projects?
  Yes, participants will build a complete real-world ML system including deployment, automation, and monitoring.
• Which tools will I learn?
  Git, DVC, MLflow, Docker, FastAPI, Kubernetes, GitHub Actions, AWS services, Prometheus, and Grafana.
• Is this workshop suitable for beginners?
  It is best suited for learners with foundational machine learning or programming knowledge who want to move toward production ML engineering.