| 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
Tools, Techniques, or Platforms Covered
Python Scikit-learn TensorFlow Keras Pandas NumPy Matplotlib XGBoost
Real-World Applications
- Apply MLOps skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Machine Learning competencies
- Solve industry-relevant problems using MLOps methodologies and tools
- Contribute to open-source projects and collaborative research in Machine Learning
- Prepare for competitive examinations, interviews, and professional certifications in Machine Learning
Who Should Attend & Prerequisites
- Students pursuing degrees in Machine Learning, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Machine Learning roles
- Researchers and academicians looking to adopt modern techniques in Machine Learning
- Entrepreneurs, freelancers, and self-learners interested in practical Machine Learning knowledge
Certification
