Infrastructure for MLOps on Ubuntu Linux

🎯 Target Audience

• This course is aimed at developers, data engineers, data scientists, and IT professionals who want to implement and optimize MLOps practices in Ubuntu Linux-based environments. It is also suitable for teams looking to enhance their AI/ML infrastructures using open-source technologies.

📚 Course Description

The “MLOps on Ubuntu Linux” course provides a practical and detailed guide to implementing MLOps in production environments using open-source tools and technologies. Throughout this course, participants will learn to design, deploy, and optimize MLOps infrastructures, from creating machine learning pipelines to efficiently managing resources in the cloud and at the edge. The course combines theory and practice, allowing attendees to acquire skills to tackle MLOps challenges in the industry.

🧠 What You’ll Learn

• Basic concepts of MLOps: Understand the fundamental principles and importance of MLOps in AI/ML projects.
• Design and deployment of MLOps architectures: Learn to design and deploy MLOps infrastructures using open-source tools in cloud and edge environments.
• Resource optimization: Master techniques to maximize efficient use of resources in production environments.
• Implementation of ML pipelines: Create and integrate robust pipelines using tools like Kubeflow on Ubuntu.
• Model management in production: Learn to monitor, maintain, and manage the lifecycle of models in production.

📋 Course Syllabus

Module 1: Introduction to MLOps

• 1.1. Basic Concepts of MLOps
  • Definition and objectives
  • Benefits and challenges of implementing MLOps
• 1.2. The role of MLOps in AI and ML
  • The MLOps lifecycle
  • Comparison with DevOps
• 1.3. Essential tools in MLOps
  • Introduction to open-source tools (KubeFlow, MLFlow, etc.)
• Exercise 1:
  • Setting up a development environment on Ubuntu for MLOps.
  • Installing basic tools like Docker, Kubernetes, and Kubeflow.

Module 2: Modern Infrastructure for MLOps

• 2.1. AI/MLOps Architecture in the Cloud
  • Deployment options: private, public, and multi-cloud.
  • Setting up an AI architecture in the cloud with Ubuntu.
• 2.2. Infrastructure at the edge
  • AI architecture at the edge: advantages and challenges.
  • Use cases and practical examples.
• Exercise 2:
  • Deploying an MLOps architecture in a cloud environment using Ubuntu.
  • Basic setup for an Edge AI environment.

Module 3: MLOps Architecture Design

• 3.1. Migration strategies to open-source solutions
  • Benefits of migrating to open-source solutions.
  • Strategies for transitioning from closed to open-source infrastructures.
• 3.2. Resource optimization in MLOps
  • Techniques to avoid underutilization of resources.
  • Tools to monitor and optimize resource usage on Ubuntu.
• Exercise 3:
  • Migrating an ML pipeline to an open-source architecture on Ubuntu.
  • Setting up and optimizing GPU usage in an Ubuntu environment.

Module 4: MLOps Processes and Tools

• 4.1. Implementation of ML pipelines
  • Introduction to creating pipelines using tools like Kubeflow.
  • Integration of pipelines in a production environment.
• 4.2. Monitoring and maintenance
  • Techniques for monitoring models in production.
  • Model maintenance and version management.
• Exercise 4:
  • Creating an ML pipeline in Kubeflow on Ubuntu.
  • Implementing a continuous monitoring system for ML models.

Module 5: Advanced MLOps Practices

• 5.1. Model optimization for inference
  • Strategies to improve efficiency in inference.
  • Using specific tools for model optimization on Ubuntu.
• 5.2. Model lifecycle management
  • Implementing strategies for complete model lifecycle management.
  • Advanced use cases of ML in production.
• Exercise 5:
  • Optimizing a model for inference in an Ubuntu environment.
  • Implementing a model lifecycle management system in Kubeflow.

Module 6: Practical Cases and Workshops

• 6.1. Applying MLOps in a real project
  • Defining a practical use case.
  • Complete implementation of the MLOps lifecycle.
• 6.2. Personalized workshops and Q&A
  • Live workshops with experts to solve doubts.
  • Customization of acquired knowledge in specific industry cases.
• Exercise 6:
  • Develop and deploy a complete MLOps project using the tools and knowledge acquired.
  • Participate in a live workshop to resolve doubts and improve the project’s architecture.

🧰 Materials and Requirements

• Materials: Participants will receive access to digital materials, user guides, code examples, and other relevant resources.
• Requirements: Basic knowledge of machine learning, Python, and Linux.

💻 Enrollment

To enroll in this course, complete the form found below on this same page.