Machine Learning Training in Ameerpet | Visualpath

1. Machine Learning Operations Training ONLINE TRAINING

2. Introduction to MLOps Definition of MLOps • Definition: MLOps is the set of practices that streamline the deployment, management, and maintenance of machine learning models in production. • Significance: Bridging the gap between data science and IT operations for efficient and effective ML workflows. WWW.Visualpath.in

3. Key Concepts • CI/CD for ML • Model Deployment • Monitoring and Logging • Scalability and Performance Optimization • Security and Compliance • Model Governance and Explainability • Version Control for ML • Infrastructure and Environment Setup • Collaboration and Communication • Conclusion

4. CI/CD for ML • CI/CD in ML: Adapting continuous integration and continuous deployment principles to automate the ML pipeline. • Ensures consistency, reproducibility, and rapid delivery of machine learning models. WWW.Visualpath.in

5. Model Deployment • Deployment Strategies: Batch processing (offline) vs. real-time (online) deployment. • Cloud Platforms: Leveraging AWS Sage Maker, Azure ML for scalable and efficient model deployment. WWW.Visualpath.in

6. Monitoring and Logging • Real-time Monitoring: Continuous monitoring for model performance, issue detection, and addressing model drift. • Logging: Capturing relevant information for troubleshooting and analysis. WWW.Visualpath.in

7. Scalability and Performance Optimization • Scaling Infrastructure: Adapting infrastructure for varying workloads and considerations for distributed computing. • Optimizing Models: Techniques for resource efficiency, faster inference, and improved performance. WWW.Visualpath.in

8. Security and Compliance • Data Privacy: Safeguarding sensitive data and ensuring compliance with regulations (e.g., GDPR). • Access Controls: Managing permissions to prevent unauthorized usage. WWW.Visualpath.in

9. Model Governance and Explainability • Governance Practices: Establishing policies for model development, deployment, and monitoring. • Explainability and Interpretability: Communicating how models make decisions, ensuring transparency. WWW.Visualpath.in

10. Version Control for ML • Git and Versioning: Utilizing Git for tracking changes in code, data, and model versions. • Maintaining reproducibility and facilitating collaboration. WWW.Visualpath.in

11. Infrastructure and Environment Setup • Development Environments: Creating consistent and reproducible environments for model development and testing. • Containers and Virtualization: Using Docker for encapsulating models and dependencies. WWW.Visualpath.in

12. Collaboration and Communication • Interdisciplinary Collaboration: Fostering effective communication between data scientists, data engineers, and IT operations. • Documentation: Maintaining clear and comprehensive documentation for knowledge transfer. WWW.Visualpath.in

13. Conclusion • Recap of key concepts in MLOps. • Emphasize the importance of implementing these concepts for successful machine learning operations. WWW.Visualpath.in

14. CONTACT Machine Learning 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

15. WWW.Visualpath.in