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MLOps Fundamentals
BeginnerFree LearningMachine Learning

MLOps Fundamentals

by UptoSkills Team, Swayam Patel

Missions

10

Quests

60

Games

29

XP

340

Coins

35

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
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Key Benefits

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Gain 340 XP to unlock badges and achievements

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Earn 35 coins to redeem rewards

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About This League

This league establishes the essential MLOps practices required to move machine learning models from experimentation to production. It demystifies the lifecycle of an ML model, focusing on reproducibility and efficient deployment for real world applications. Students will gain hands-on experience with industry standard tools and techniques.

What you will learn

Upon completion, you will be able to:

  • Set up a Python environment and build your first machine learning model with Pandas and basic visualization.
  • Convert Jupyter notebooks into robust Python scripts with argument parsing and logging.
  • Implement effective data and code versioning strategies using Git for reproducible ML workflows.
  • Containerize ML applications using Docker for consistent and isolated environments.
  • Track model experiments with MLflow, build ML pipelines, and automate with CI/CD concepts.
  • Deploy machine learning models as REST APIs and understand fundamental monitoring techniques.

What this league covers

ModuleWhat it covers
Foundation: Your First ML ModelSetting up Python, using Pandas DataFrames, basic data exploration and visualization, training and evaluating a simple model, saving and loading models.
Notebook Mastery: From Exploration to ScriptStructuring Jupyter notebooks, refactoring into functions, converting to Python scripts with argparse, basic logging, and creating reusable modules.
Data Versioning EssentialsUnderstanding data versioning importance, manual strategies, Git for data files, naming conventions, basic Git commands, and data organization.
Code Versioning with GitGit fundamentals, repositories, commits, branches, cloning, staging, merging, conflict resolution, and remote operations.
Containerization Basics: Docker FundamentalsIntroduction to containerization, Docker installation, images, containers, writing Dockerfiles, building images, and using volumes.
Model Experiment TrackingImportance of experiment tracking, manual methods, using MLflow for logging parameters, metrics, artifacts, and visualizing runs.
Building an ML PipelineUnderstanding ML pipelines, designing flows, orchestrating steps with Python scripts, passing data, error handling, and code structuring.
Introduction to CI/CD for MLConcepts of CI/CD, automating code checks and deployments, Git triggers, basic build steps, and introduction to GitHub Actions.
Model Deployment StrategiesCrucial aspects of deployment, saving models, creating REST APIs with Flask, packaging with Docker, and running containerized APIs locally.
Monitoring and IterationMonitoring deployed models, performance metrics, data and model drift, collecting logs, and the concept of model retraining.

Students are now equipped to build and deploy machine learning models systematically.

A look inside

MLOps Fundamentals screen 1MLOps Fundamentals screen 2MLOps Fundamentals screen 3MLOps Fundamentals screen 4

Prerequisites

  • Comfortable writing Python, including lists, dictionaries and functions.
  • Understand supervised learning, training/test splits and overfitting.
  • Confident on the command line — navigating directories, running commands, editing files.
  • Have deployed or run an application somewhere, even if only on your own machine.

Learning Objectives

  • Understand the core principles and benefits of MLOps in streamlining machine learning workflows.
  • Identify and describe the key stages of a typical machine learning lifecycle, from data preparation to model deployment and monitoring.
  • Implement basic version control for machine learning code and models using tools like Git.
  • Describe the purpose and functionality of containerization technologies like Docker for packaging machine learning models and their dependencies.
  • Explain the fundamental concepts of automated model training and evaluation pipelines.
  • Recognize the importance of model monitoring and describe common metrics for assessing model performance in production.