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MLOps Training in Hyderabad | MLOps Course in Hyderabad

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Visualpath offers an effective MLOps Online Training Program. To schedule a free demo, simply reach out to us at +91-9989971070. Visit WhatsApp: – PowerPoint PPT presentation

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Title: MLOps Training in Hyderabad | MLOps Course in Hyderabad


1
(MLOps)Machine Learning VSDevOps
2
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.

3
 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.

4
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.

5
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

6
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.

7
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.

8
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.

9
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.

10
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.

11
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
12
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