A Complete MLops Journey
IntermediateLevel
356+Students Enrolled
2 Hrs Duration
4.6Average Rating
About this Course
- The course guides from ML experiment tracking to scalable deployment, covering data & model versioning, CI/CD, containerization, and drift monitoring.
- Through hands-on labs, build Level 0 & Level 1 MLOps architectures, automate pipelines with GitHub Actions, and push models to AWS or GCP managed services.
- Learn to set up feature stores, implement real-time monitoring dashboards, and alerting systems that surface data or model drifts before they impact users.
Learning Outcomes
End-to-End MLOps
Design ML pipelines from data ingestion to deployment and monitoring
CI/CD Automation
Automate testing and release of models via Git actions and containers
Drift Monitoring
Detect data and model drift with dashboards and alerting loops
Who Should Enroll
- Data scientists ready to productionize notebooks into version-controlled, continuously deployed ML services.
- ML engineers tasked with automating pipelines, containerizing models, and setting up cloud monitoring for critical apps.
- Backend developers entering ML who need a clear roadmap to add CI/CD, Docker, and MLOps workflows to existing projects.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Course Introduction
2. The Challenges in ML Workflow
3. General Challenges
4. Blueprint of Level 0 Architecture
5. Hands-on with Level 0 Architecture Part - I
6. Hands-on with Level 0 Architecture Part - II
1. Real-time Prediction and Batch Time Prediction
2. Model Deployment in Streamlit
3. Understanding Data Drift and Concept Drift
4. Drawbacks of Level 0 Architecture
1. Introduction to Cloud Platform
2. ML Framework
3. Blueprint of Level 1 Architecture Part I
4. Blueprint of Level 1 Architecture Part II
5. Best Practices for MLOps Mastery!
Get this Course Now
With this course you’ll get
- 2 Hours
Duration
- Apoorv Vishnoi
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
MLOps (Machine Learning Operations) focuses on managing ML models through their lifecycle—training, deployment, and monitoring—whereas DevOps is centered around software development and delivery pipelines.
An MLOps pipeline typically includes data ingestion, preprocessing, model training, versioning, deployment, monitoring, and retraining workflows.
Data drift refers to changes in input data over time. It can degrade model accuracy, making continuous monitoring essential in production environments.
Data drift refers to changes in input data, while concept drift refers to changes in the relationship between input and output. Both can degrade model performance and must be addressed in production.
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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