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MLOps Tutorial 2024 - Learn MLOps Online for Free

7 Modules27 Articles199 Learners

Dive into the fundamentals with our MLOPs tutorial. Learn MLOps best practices and streamline your ML operations for enhanced productivity and reliability.

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Explore our MLOps Tutorial, your pathway to mastering the realm of Machine Learning Operations. If you're a beginner wondering how to learn MLOps, you're in the right place. Our MLOps tutorial for beginners is designed to demystify the world of machine learning operations. Learn MLOps essentials, from managing ML models to automating workflows.

We'll guide you through the entire process, ensuring you gain a solid foundation in this crucial field. By the end of this MLOps tutorial, you'll be equipped with the knowledge and skills necessary to streamline and optimize your machine learning projects. Start your journey to learn machine learning operations today.

The modules covered in this MLOPs Tutorial are:

  1. Introduction to MLOps
  2. Version Control for ML
  3. Docker for ML
  4. Continuous Integration and Delivery (CI/CD) for ML
  5. Monitoring and Logging for ML
  6. Scaling ML Workloads
  7. Ethical Considerations in MLOps

Course Curriculum

Module 1Introduction to MLOps

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Introduction to MLOps and Its Importance

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Challenges in ML Model Development and Deployment

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Key Principles of MLOps (Machine Learning Operations)

Module 2Version Control for ML

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Introduction to Git and GitHub - MLOPs

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Best Practices for Version Controlling ML Projects

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Branching and Merging Strategies in MLOPs

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Managing Data and Model Artifacts with Git LFS

Module 3Docker for ML

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Introduction to Docker and Containerization in MLOPs

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Building Docker Images for ML Applications

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Running Docker Containers Locally and on the Cloud - MLOPs

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Best Practices for Containerization of ML Applications

Module 4Continuous Integration and Delivery (CI/CD) for ML

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CI/CD for Machine Learning

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Setting up a CI/CD Pipeline Using Jenkins

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Automated Testing and Validation of ML Models

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ML Models Deployment in Kubernetes

Module 5Monitoring and Logging for ML

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Importance of MLOPs Monitoring and Logging

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Setting up Monitoring and Logging Infrastructure

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Defining Metrics and Alerts for ML models

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Visualizing and Analyzing Model Performance

Module 6Scaling ML Workloads

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Introduction to Distributed Computing for ML

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Setting up a Distributed ML Environment with Apache Spark

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Scaling ML workloads: Docker Swarm vs Kubernetes

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Best Practices for Scaling ML Workloads

Module 7Ethical Considerations in MLOps

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Overview of Ethical Considerations of MLOps

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Bias and Fairness in Machine Learning Models

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MLOps Security and Privacy Considerations

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Implementing Ethical Guidelines in MLOps

Summary

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