| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 6 Months |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python TensorFlow PyTorch Scikit-learn Docker Kubernetes |
About the MLOps: Machine Learning Operations Course
MLOps: Machine Learning Operations Course dives deep into Mlops Machine Learning Operations.
Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Comprehensive coverage of MLOps from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI and 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
• Practical experience with tools: Python, TensorFlow, PyTorch, Scikit-learn
• Career-oriented training for academic and professional growth in AI and Machine Learning
Course Curriculum
Module 1: AI Fundamentals, Mathematics, and MLOps Foundations
- Apply linear algebra and calculus concepts to machine learning problems
- Analyze probability distributions and statistical measures in AI systems
- Develop mathematical models to optimize machine learning algorithm performance
Module 2: Data Engineering, Preprocessing, and Feature Pipelines
- Design data pipelines to handle large-scale datasets and ensure data quality
- Implement data preprocessing techniques to handle missing values and outliers
- Configure feature engineering workflows to extract relevant features from datasets
Module 3: Model Architecture, Algorithm Design, and MLOps Methods
- Evaluate different machine learning algorithms for classification and regression tasks
- Develop neural network architectures for deep learning applications
- Optimize model hyperparameters using grid search and random search techniques
Module 4: Training, Hyperparameter Optimization, and Evaluation
- Train machine learning models using supervised and unsupervised learning techniques
- Implement hyperparameter tuning using Bayesian optimization and gradient-based methods
- Assess model performance using metrics such as accuracy, precision, and recall
Module 5: Deployment, MLOps, and Production Workflows
- Deploy machine learning models using containerization and orchestration tools
- Configure model serving pipelines for real-time inference and prediction
- Develop monitoring and logging workflows to track model performance in production
Module 6: Ethics, Bias Mitigation, and Responsible AI Practices
- Analyze bias in machine learning datasets and models using fairness metrics
- Develop strategies to mitigate bias and ensure fairness in AI systems
- Implement transparency and explainability techniques to build trust in AI decision-making
Module 7: Industry Integration, Business Applications, and Case Studies
- Apply machine learning to real-world business problems in industries such as healthcare and finance
- Evaluate the economic and social impact of AI adoption in various sectors
- Develop business cases for AI-powered solutions and communicate results to stakeholders
Tools, Techniques, or Platforms Covered
Python TensorFlow PyTorch Scikit-learn Docker Kubernetes
Real-World Applications
- Apply MLOps skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI and Machine Learning competencies
- Solve industry-relevant problems using MLOps methodologies and tools
- Contribute to open-source projects and collaborative research in AI and Machine Learning
- Prepare for competitive examinations, interviews, and professional certifications in AI and Machine Learning
Who Should Attend & Prerequisites
- Designed for Professionals.
- Designed for Students.
- Foundational knowledge of artificial intelligence and familiarity with core concepts recommended.
- Mentorship by industry experts and NSTC faculty.
Certification
