MLOps Training in Hyderabad | Machine Learning Operations Training

1. MLOPS Machine Learning Operations

2. Introduction to MLOps • Definition of MLOps: Combining Machine Learning and DevOps practices to streamline the ML lifecycle. • Importance: Ensures seamless integration of ML models into production systems, improves efficiency, and reduces time-to-market.

3. Key Components of MLOps • Model Development: Data preparation, feature engineering, and model training. • Model Deployment: Integration into production systems, scaling, and continuous deployment. • Model Monitoring: Tracking performance, detecting issues, and retraining models. • Collaboration & Governance: Ensuring cross-functional teamwork and adhering to compliance standards.

4. MLOps Workflow Overview • Diagram of MLOps Workflow: • Data Collection and Preparation • Model Training and Validation • Model Deployment • Model Monitoring and Management • Feedback Loop and Retraining

5. Best Practices in MLOps • Automation: Use of CI/CD pipelines for continuous integration and deployment. • Version Control: Tracking changes in data, code, and models. • Monitoring and Logging: Implementing robust monitoring for performance and operational metrics. • Security: Ensuring data and model security, compliance with regulations.

6. Tools and Technologies in MLOps • Version Control: Git, DVC • CI/CD Tools: Jenkins, GitLab CI, CircleCI • Containerization: Docker, Kubernetes • Monitoring Tools: Prometheus, Grafana, Mlflow • Collaboration Platforms: GitHub, GitLab, JIRA

7. Conclusion • Summary: Recap of the key points covered in the presentation. • Future Trends: Emerging trends in MLOps, such as AI-driven automation and advanced monitoring techniques.

8. CONTAC Machine Learning Operations Training Address:- Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1 Ph. No: +91-9989971070 Visit:www.visualpath.in E-Mail: online@visualpath.in

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