Introduction to MLOps Foundation Certification
The MLOps Foundation Certification is designed to equip professionals with essential skills and knowledge in Machine Learning Operations (MLOps). As AI and machine learning models increasingly move into production environments, managing and operationalizing them effectively is critical. This certification, introduced by DevOpsSchool in association with renowned trainer Rajesh Kumar from www.RajeshKumar.xyz, provides a solid foundation in MLOps principles, helping students master the practices needed to scale machine learning models while ensuring smooth integration with DevOps processes.
Who Should Attend?
- Data Scientists transitioning to operational roles
- Machine Learning Engineers
- DevOps Engineers with an interest in AI/ML
- IT Professionals aiming to integrate AI/ML into production
- AI/ML team managers
Course Objectives
By the end of this certification, students will:
- Understand the core principles and practices of MLOps.
- Learn to automate the lifecycle of machine learning models.
- Master the techniques of model training, deployment, monitoring, and governance.
- Get hands-on experience with MLOps tools and technologies.
- Integrate machine learning models with DevOps pipelines.
- Ensure scalable and reliable machine learning solutions.
Course Agenda
1. Introduction to MLOps
- What is MLOps and Why is it Important?
- MLOps vs DevOps: Key Differences
- MLOps Lifecycle: Development to Production
- Case Studies of Successful MLOps Implementations
2. Understanding Machine Learning Pipelines
- Building and Automating ML Pipelines
- Data Preparation and Feature Engineering
- Model Training, Tuning, and Validation
- Handling Big Data in ML Pipelines
- Continuous Integration (CI) in ML
3. Deploying Machine Learning Models
- Model Packaging and Containerization (Docker, Kubernetes)
- Real-time vs Batch Model Serving
- Model Versioning and Model Registry
- Best Practices for Model Deployment
- Integration with Cloud Platforms (AWS, GCP, Azure)
4. Monitoring Machine Learning Models
- Model Performance Metrics
- Model Drift and Retraining
- Automated Monitoring Solutions
- Logging and Debugging in MLOps
- Performance Optimization Techniques
5. Governance and Compliance in MLOps
- Data Privacy and Security in MLOps
- Ethical Considerations in Machine Learning
- Managing Model Risks
- Regulatory Compliance for AI/ML Models
- Auditing and Documentation in MLOps
6. MLOps Tools and Technologies
- Introduction to Key MLOps Tools (MLflow, Kubeflow, TFX, etc.)
- CI/CD Tools for MLOps (Jenkins, GitLab, etc.)
- Model Serving Platforms (Seldon, TensorFlow Serving)
- Experiment Tracking and Reproducibility
- Toolchain Integration and Best Practices
7. Hands-on Lab Sessions
- Building a Complete MLOps Pipeline from Scratch
- Experimenting with CI/CD for Machine Learning
- Model Deployment and Monitoring using Kubernetes
- Automating Retraining and Model Updates
8. Final Project: End-to-End MLOps Implementation
- Students will complete a capstone project that covers the end-to-end implementation of an MLOps pipeline. They will be graded on model training, deployment, monitoring, and governance.
- Feedback from Trainer Rajesh Kumar.
Certification Benefits
- Industry-Recognized Certification: Upon completion, students will receive a globally recognized MLOps Foundation Certification from DevOpsSchool.
- Hands-On Learning: The course includes practical labs and real-world project work, ensuring that students have experience applying what they learn.
- Career Advancement: The certification positions students to take on roles in MLOps, AI/ML integration, and related areas.
- Support and Guidance: Direct access to an experienced instructor, Rajesh Kumar, for mentorship and advice.
Trainer Profile: Rajesh Kumar
Rajesh Kumar, with over 15 years of experience in DevOps and MLOps, is a well-known trainer and expert in the field. His association with DevOpsSchool has helped thousands of students and professionals gain practical knowledge in cutting-edge technologies. His expertise covers various domains, including MLOps, DevOps, and cloud computing.
Certification Exam Details
- Format: Multiple-choice questions (MCQs)
- Duration: 60 minutes
- Passing Criteria: 70% minimum score
- Exam Availability: Online (via DevOpsSchool portal)
How to Register?
To register for the MLOps Foundation Certification, visit DevOpsSchool MLOps Certification and follow the steps provided.
