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MLOps Complete Guide to Implementation and Deployment

MLOps Complete Guide to Implementation and Deployment

$199.00
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MLOps Complete Guide to Implementation and Deployment



Course Overview

This comprehensive course provides a detailed guide to implementing and deploying Machine Learning Operations (MLOps) in real-world applications. Participants will gain hands-on experience and in-depth knowledge of MLOps principles, tools, and best practices.



Course Objectives

  • Understand the fundamentals of MLOps and its importance in Machine Learning
  • Learn how to design and implement MLOps pipelines for efficient model development and deployment
  • Gain practical experience with MLOps tools and technologies
  • Understand how to monitor and maintain deployed models
  • Learn how to ensure model reliability, scalability, and security


Course Outline

Module 1: Introduction to MLOps

  • What is MLOps?
  • Importance of MLOps in Machine Learning
  • MLOps vs DevOps
  • Benefits of MLOps
  • Challenges in implementing MLOps

Module 2: MLOps Principles and Best Practices

  • MLOps principles
  • Version control for data and models
  • Collaboration and communication in MLOps
  • Automated testing and validation
  • Continuous Integration and Continuous Deployment (CI/CD)

Module 3: MLOps Tools and Technologies

  • Overview of MLOps tools
  • TensorFlow Extended (TFX)
  • MLFlow
  • Kubeflow
  • Other MLOps tools and technologies

Module 4: Designing and Implementing MLOps Pipelines

  • Designing MLOps pipelines
  • Data preparation and preprocessing
  • Model development and training
  • Model evaluation and validation
  • Model deployment and serving

Module 5: Model Monitoring and Maintenance

  • Importance of model monitoring
  • Types of model monitoring
  • Model performance monitoring
  • Model explainability and interpretability
  • Model updating and retraining

Module 6: Ensuring Model Reliability, Scalability, and Security

  • Model reliability
  • Model scalability
  • Model security
  • Best practices for ensuring model reliability, scalability, and security

Module 7: Advanced MLOps Topics

  • AutoML and MLOps
  • MLOps for edge devices
  • MLOps for real-time applications
  • MLOps for large-scale applications

Module 8: Hands-on Projects and Case Studies

  • Hands-on projects
  • Case studies
  • Group discussions and presentations


Course Features

  • Interactive and engaging: Video lectures, hands-on projects, and group discussions
  • Comprehensive and up-to-date: Covers the latest MLOps tools and best practices
  • Personalized learning: Flexible pacing and lifetime access to course materials
  • Practical and real-world: Hands-on projects and case studies
  • High-quality content: Expert instructors and high-quality video lectures
  • Certification: Receive a certificate upon completion issued by The Art of Service
  • Flexible learning: Learn at your own pace and on your own schedule
  • User-friendly: Easy to navigate and access course materials
  • Mobile-accessible: Access course materials on-the-go
  • Community-driven: Join a community of learners and experts
  • Actionable insights: Gain practical knowledge and skills
  • Hands-on projects: Apply MLOps principles and tools to real-world projects
  • Bite-sized lessons: Learn in manageable chunks
  • Lifetime access: Access course materials for a lifetime
  • Gamification: Engage with interactive elements and track progress
  • Progress tracking: Monitor your progress and stay motivated


Certification

Upon completion of the course, participants will receive a certificate issued by The Art of Service.

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