Master Program
MLOps & Cloud Computing
- 4.721 students
- Last updated : March 6, 2026
Overview
The MLOps & Cloud Computing Master Program is designed to equip learners with the skills required to take machine learning models from development to production. This program focuses on model deployment, automation, CI/CD, cloud-native ML pipelines, monitoring, and end-to-end lifecycle management—the exact skills companies expect from modern ML Engineers and MLOps Engineers.
Learners gain hands-on experience with major cloud platforms (AWS, Azure, GCP), containerization, orchestration tools, experiment tracking, model monitoring, and enterprise-grade AI deployment workflows.
Ideal for Data Scientists, ML Engineers, DevOps Engineers, Software Developers, and anyone who wants to specialize in production-level AI systems.
Program Objective
- Understand the complete lifecycle of ML systems from training to deployment.
- Build automated pipelines for training, testing, deployment, and monitoring.
- Learn containerization, orchestration, and scalable cloud infrastructures.
- Work on real-world MLOps & cloud deployment projects across industries.
- Implement CI/CD for ML using modern DevOps and MLOps tools.
- Deploy models using FastAPI, Docker, Kubernetes, and cloud services.
- Track experiments, model versions, performance metrics, and drift.
What You Will Learn
- Traditional ML vs. MLOps
- ML lifecycle: development → deployment → monitoring
- Data versioning & model versioning
- Experiment tracking workflows
- Reproducibility and automation
- Pipeline-driven ML development
- Feature engineering pipelines
- AutoML concepts
- Distributed model training (introductory)
- Linux & shell scripting
- Git & GitHub workflows
- CI/CD basics and YAML pipelines
- DevOps principles applied to ML
- Deploying ML models using Flask & FastAPI
- Batch, streaming, and real-time inference
- REST APIs & microservices
- Scalable inference strategies
- Optimizing latency & throughput
- Docker for ML workloads
- Containerizing ML apps with APIs & dependencies
- Kubernetes fundamentals
- Kubernetes for ML: scaling, load balancing, secrets
- KServe, Seldon, BentoML (introduction)
- MLflow for experiment tracking
- Model registry & version control
- Data drift & concept drift detection
- Model performance monitoring
- Logging, alerting, and dashboarding with Prometheus & Grafana
- AWS: S3, Lambda, SageMaker, ECR/ECS, CloudWatch, Step Functions
- Azure: ML Studio, AKS, ACR, Blob Storage
- GCP: Vertex AI, Cloud Run, BigQuery, Cloud Functions
Learn to deploy, manage, and monitor ML pipelines using cloud-native services.
- CI/CD for ML using GitHub Actions / GitLab CI / Jenkins
- Automated data ingestion
- Automated model training + testing
- Container-based deployment
- Model monitoring & feedback loops
- Retraining workflows
Tools & Technologies Covered
MLflow, DVC, Airflow, Kubeflow, Prefect
AWS, Azure, GCP
Docker, Kubernetes
FastAPI, Flask, NGINX, Gunicorn, BentoML
Prometheus, Grafana, ELK Stack
GitHub Actions, Jenkins, Terraform (optional)
Python, Bash
Projects You Will Build
Projects span domains such as retail, healthcare, finance, manufacturing, insurance, and IoT.
Career Outcomes
This Master Program prepares you for advanced AI system engineering roles:
Why Learners Choose This Program
- Hands-on development of real production pipelines
- Covers all major clouds: AWS, Azure, GCP
- Project-based learning with enterprise datasets
- Curriculum aligned with modern MLOps roles
- Learn both ML engineering + cloud infra
- Build a job-ready MLOps portfolio in GitHub
- Career support, mock interviews, and resume guidance
Instructor
Data Science Expert
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Testimonial
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