Python for MLOps

You can learn Python on expert level based on real MLOps tasks

In this course, you will learn how to use Python for real work in MLOps and Machine Learning projects. You do not need to be an expert to start. Step by step, you will set up Python, write clear scripts, and work with data the way MLOps engineers do every day.

You will use NumPy and Pandas for data tasks, build small CLI tools to automate work, and learn safe patterns for files, paths, configs, logging, and errors. You will practice tests with pytest and unittest, use JSON and YAML for settings, call HTTP APIs, and package simple utilities for your team.

By the end, you will be ready to solve common MLOps problems, move data with confidence, and hand off reliable Python code into CI/CD and real projects. This course is your starting point for using Python in machine learning and operations.

Course Content

  • Course Introduction
  • About Your Instructor
  • Course Structure
  • What is Python
  • Python vs Other Programming Languages
  • GitHub repositories with MLOps projects
  • Differences between Python versions 2 and 3
  • Python Installation on MS Windows
  • Python Installation on Linux
  • Python Installation on macOS
  • Installation of Python Libraries
  • Required Python Libraries for this Course
  • Additional Tools
  • Basic Syntax
  • Basic Data Types
  • Strings
  • F-strings and String Formatting
  • Collections
  • Naming Conventions
  • Scope & namespaces
  • Comparison Operators
  • Conditional Statements
  • Iteration and Loops
  • List comprehensions
  • Function Definition Syntax
  • Lambda Functions
  • Typing and Linting
  • Finding Text Patterns
  • Regular Expressions
  • Creating modules & packages
  • Standard Modules
  • Third-party packages
  • Packages
  • OOP Basic Concepts
  • Class Definition Syntax
  • Object lifecycle (init, del)
  • Attributes & @property
  • Methods & @classmethod / @staticmethod
  • Class Objects
  • Handling exceptions (try/except/else/finally)
  • Built-in Exceptions
  • User-defined Exceptions
  • Raising exceptions
  • Introduction to Testing
  • Testing Conventions
  • Using unittest and pytest
  • Using plain assert in pytest
  • Writing Test Classes
  • Test Classes vs. Test Functions
  • Parameterizing Tests
  • Test Failure Output
  • File I/O
  • Context Managers
  • Working with paths (pathlib)
  • Environment Variables
  • Subprocess & shell commands
  • Introduction to NumPy
  • Creating and Manipulating Arrays
  • Array Indexing, Slicing, and Reshaping
  • Vectorized Operations and Basic Math
  • Introduction to Pandas
  • Loading Data with Pandas
  • Exploring and Inspecting DataFrames
  • Transforming and Cleaning Data
  • Exporting DataFrames to Files
  • Applying Functions and Simple Data Validation
  • Quick Visualization with Pandas
  • Basic Logging
  • Logging Levels
  • Introduction to Serialization
  • Working with JSON
  • Working with YAML
  • Pickle and Model/Object Serialization
  • Interacting with the System
  • Config & structured data (INI/TOML/YAML)
  • HTTP clients (requests, httpx)
  • Automation with CLI Tools
  • Single-file scripts & entry points
  • Command-line apps with argparse
  • Environment Management

Start learning high demand tech skills today

About Your Instructor

Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.

In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.

You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.

All courses are developed by experienced instructors with over 10 years of real-world industry expertise. We focus on delivering practical, up-to-date content – not just collecting enrollments, so that every course gives you real value.

Our courses meet high academic standards, and we’re actively working on certification to ensure they align with recognized best practices.

Each course includes video lectures, hands-on labs with screen recordings, quizzes, reading materials, GitHub repository with real project code, and a capstone project. This structure is designed to help you build practical, in-demand skills and knowledge that employers care about.

However, if you’re not satisfied for any reason, you can request a refund in accordance with our Refund Policy – your satisfaction matters to us.

It’s not just skills. It’s your next chapter.

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