Machine Learning Training in Ameerpet | Visualpath

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Machine Learning Operations Training

• ONLINE TRAINING

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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.

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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

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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.

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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.

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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.

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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.

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Security and Compliance

• Data Privacy Safeguarding sensitive data and
  ensuring compliance with regulations (e.g.,
  GDPR).
• Access Controls Managing permissions to prevent
  unauthorized usage.

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Model Governance and Explainability

• Governance Practices Establishing policies for
  model development, deployment, and monitoring.
• Explainability and Interpretability
  Communicating how models make decisions, ensuring
  transparency.

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Version Control for ML

• Git and Versioning Utilizing Git for tracking
  changes in code, data, and model versions.
• Maintaining reproducibility and facilitating
  collaboration.

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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.

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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.

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Conclusion

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

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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_at_visualpath.in
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