MLOps Training in Hyderabad | MLOps Course in Hyderabad

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(MLOps)Machine Learning VSDevOps
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Introduction

• Software development landscape is ever-changing.
• Machine learning (ML) introduces new complexities
  and opportunities.
• DevOps, the standard for streamlining software
  delivery, wasn't designed for ML models.
• Enter MLOps an extension of DevOps addressing
  the unique needs of deploying and managing ML
  applications.
• This presentation explores both, highlighting
  their synergies and how they bridge the gap
  between development and operations in the ML
  pipeline.

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 Core Principles

• DevOps and MLOps share core principles
• Automation
• Collaboration
• Continuous improvement
• Leverage tools and practices to unify development
  and operations.
• Ensure smooth transition from code to production.
• Emphasize infrastructure as code (IaC) and
  continuous integration and continuous delivery
  (CI/CD) pipelines for efficient delivery.

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DevOps Streamlining Traditional Software
Development 

• Fosters collaboration between developers,
  testers, and operations teams.
• Automates tasks like code building, testing, and
  deployment.
• Leads to faster delivery cycles and improved
  software quality.
• Practices
• Infrastructure as code (IaC) Define
  infrastructure in code for automated provisioning
  and configuration management.
• CI/CD pipelines Automate the software delivery
  process, integrating code changes, running tests,
  and deploying to production.

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MLOps Tailored for the Machine Learning Lifecycle

• Extends DevOps principles to machine learning.
• Data scientists join developers and operations to
  manage the entire ML model lifecycle.
• This includes
• Data management and model training
• Deployment
• Monitoring
• Governance

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Key Differences Data, Models, and More

• Focus DevOps - traditional software
  applications, MLOps - machine learning models.
• Data Management MLOps places a strong emphasis
  on data versioning and management for optimal
  model performance.
• Model Versioning Crucial in MLOps for rollbacks
  and comparisons.
• Performance Monitoring MLOps prioritizes
  continuous monitoring of model performance in
  production to detect drift and ensure
  effectiveness.

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Benefits of MLOps

• Reduced Time to Market Streamlined workflows
  accelerate delivering ML models to production.
• Improved Model Performance Ensures data quality,
  facilitates model experimentation, and enables
  continuous monitoring for optimal performance.
• Enhanced Governance and Explainability Version
  control and monitoring improve model traceability
  and understanding.
• Increased Collaboration Fosters collaboration
  between data scientists, developers, and
  operations, leading to more efficient model
  development and deployment.

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Challenges of MLOps

• Cultural Shift Aligning development, data
  science, and operations teams requires a cultural
  shift towards collaboration and shared goals.
• Tool Integration Integrating various tools and
  platforms used throughout the ML lifecycle can be
  complex.
• Monitoring and Observability Monitoring complex
  ML models in production requires specialized
  tools and expertise.

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MLOps Best Practices

• Standardize the ML Pipeline Define clear stages
  in the ML lifecycle with well-defined tools and
  processes.
• Automate ML Workflows Automate tasks like data
  cleaning, feature engineering, and model training
  to improve efficiency.
• Embrace Version Control Version control all
  artifacts (data, code, and models) for
  reproducibility and rollback capability.
• Continuous Monitoring and Alerting Continuously
  monitor model performance in production to detect
  drift and ensure model effectiveness.

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Conclusion

• DevOps and MLOps are complementary practices,
  working together to bridge the gap between
  development and operations in the software
  delivery pipeline.
• While DevOps focuses on traditional software,
  MLOps tackles the unique challenges of machine
  learning.
• By embracing both, organizations can streamline
  their ML pipelines, unlock the full potential of
  machine learning, and achieve faster innovation
  cycles and improved business outcomes.

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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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