Complete MLOps Bootcamp With End-to-End Machine Learning Projects
A Practical, Project-Based Program to Build, Deploy & Manage Production ML Systems
Course Overview
Complete MLOps Bootcamp With End-to-End Machine Learning Projects is an intensive, hands-on program designed for learners who want to take machine learning models beyond notebooks and into real-world production systems.
Rather than focusing only on model accuracy, this course emphasizes the entire machine learning lifecycle — from data preparation and experimentation to deployment, automation, and monitoring.
The program is structured around real-world workflows, helping learners understand how modern ML systems are built, versioned, deployed, and maintained in production environments using industry-standard tools.
Key Course Details
- Level: Intermediate to Advanced
- Instructor: Krish Naik
- Language: English
- Total Duration: ~50+ Hours
- Students Enrolled: 30,000+
- Rating: ⭐ 4.5 / 5
- Certificate: ✅ Yes
- Access: Lifetime (Mobile & TV)
What You Will Learn
MLOps Fundamentals & Workflow Design
- Understanding the role of MLOps in production systems
- Designing reliable ML pipelines
- Managing experiments, models, and artifacts
Experiment Tracking & Model Versioning
- Tracking model experiments and results
- Managing datasets and model versions
- Reproducibility and collaboration best practices
Containerization & Deployment
- Packaging ML applications using containers
- Deploying models as services
- Managing dependencies and environments
CI/CD for Machine Learning
- Automating ML pipelines
- Continuous training and deployment concepts
- Integrating version control with ML workflows
Cloud-Based ML Deployment
- Deploying machine learning models on cloud platforms
- Handling scalable inference and model hosting
- Understanding production-ready ML architectures
Workflow Automation & Orchestration
- Building automated data and ML pipelines
- Scheduling training and deployment workflows
- Managing dependencies between ML tasks
Monitoring & ML System Reliability
- Monitoring model performance after deployment
- Tracking data drift and system metrics
- Using dashboards and logs for production insights
Learning Style & Structure
- Strong focus on end-to-end implementation
- Concepts explained first, then applied practically
- Multiple real-world ML projects
- Step-by-step progression from development to production
- Emphasis on industry-relevant workflows
Who This Course Is For
- Data scientists who want production-ready skills
- Machine learning engineers building deployable systems
- DevOps professionals entering the ML domain
- Learners transitioning from ML theory to real-world applications
- Professionals aiming for MLOps or ML engineering roles
Why This Course Stands Out
- Focuses on real-world MLOps, not just theory
- Covers the complete ML lifecycle, not isolated tools
- Project-driven approach with production mindset
- Emphasizes automation, monitoring, and scalability
- Suitable for learners aiming for industry roles
One Honest Limitation
This course assumes you already understand basic machine learning concepts.
It is not ideal for absolute beginners looking for a first introduction to ML.
Final Takeaway
If you already know machine learning fundamentals and want to learn how models are deployed, automated, monitored, and scaled in real production environments, this MLOps bootcamp offers a practical and structured path.
It’s especially valuable for learners aiming to bridge the gap between model building and real-world ML systems.
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