MLOps: Machine Learning Operations
MLOps: Machine Learning Operations is a intermediate-level, 3 Weeks online course by NSTC. Master key concepts and practical skills in Machine Learning through hands-on projects, real-world case studies, and expert mentorship.
- ModeOnline (e-LMS)
- LevelIntermediate
- Duration3 Weeks
- Fee₹4,249
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost |
About the MLOps: Machine Learning Operations Course
MLOps is the practice of integrating machine learning workflows into production environments efficiently.
This program covers best practices for deploying, monitoring, and maintaining machine learning models in real-world applications. Participants will explore tools for continuous integration, versioning, and model management, focusing on scalability and automation.
Program Highlights
• Comprehensive coverage of MLOps from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Machine Learning
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Machine Learning
• Career-oriented training for academic and professional growth in Machine Learning
Course Curriculum
Module 1: Introduction to MLOps What is MLOps?
- Definition and importance
- Comparison with DevOps and DataOps
- Benefits and challenges
Module 2: Fundamentals of Machine Learning Supervised, Unsupervised, and Reinforcement Learning Feature Engineering
- Data preprocessing
- Feature scaling and selection
Module 3: DevOps Essentials for MLOps Version Control Systems
- Git and GitHub/GitLab
Module 4: Data Engineering in MLOps Data Pipelines
- Building reproducible data pipelines
- Tools like Apache Airflow and Prefect
Module 5: Model Deployment Deployment Strategies
- Batch, online, and hybrid inference
- A/B testing and canary deployments
Module 6: Monitoring and Maintenance Model Monitoring
- Drift detection (data and model drift)
- Performance metrics tracking
Module 7: MLOps Tools in Practice Introduction to Key Tools
- MLflow: Experiment tracking and model registry
- Kubeflow: End-to-end pipeline orchestration
- TFX: TensorFlow Extended ecosystem
