Complete Guide to Python Fundamentals for MLOps

5h 56mAdvanced2024-09-27

Authors

Pragmatic AI Labs

Alfredo Deza

Course details

This comprehensive course covers the essential Python skills for succeeding in MLOps roles. Start with the fundamentals of Python programming: data types, structures, functions, and modules. Through hands-on exercises, learn testing techniques, data manipulation, and analysis. Other topics include working with datasets using pandas and NumPy, APIs and SDKs, automation with command-line tools, and building machine learning APIs. Whether you're new to MLOps or an experienced professional, this course equips you with the foundational Python skills to excel in machine learning operations roles.

Learning objectives
Core Python programming concepts.
Data manipulation and analysis.
Containerization of ML models.
GitHub Actions for automation.

Skills covered

Machine LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Introduction to Python

1. Working with Variables and Types

02 - Lesson introduction - Working with variables and types
03 - Variables and assignments
04 - Working with different data types
05 - Conditionals and evaluations
06 - Catching and handling exceptions
07 - Lesson recap - Variables and types

2. Introduction to Data Structures

08 - Lesson introduction - Python data structures
09 - Introduction to lists
10 - Creating and iterating over lists
11 - Introduction to dictionaries
12 - Creating and iterating over dictionaries
13 - Other data structures - Tuples and sets
14 - Lesson recap - Python data structures

3. Adding and Extracting Data from Data Structures

15 - Lesson introduction - Adding and extracting data
16 - Adding data to lists
17 - Extracting data from lists
18 - Extracting data from dictionaries
19 - Lesson recap - Adding and extracting data

4. Python Functions and Classes - Working with Functions

20 - Lesson introduction - Working with functions
21 - Function structure and values
22 - Function arguments
23 - Variable and keyword arguments
24 - Lesson recap - Working with functions

5. Python Functions and Classes - Building Classes and Methods

25 - Lesson introduction - Building classes and methods
26 - Introduction to classes
27 - Using a constructor
28 - Adding methods
29 - Class inheritance
30 - Lesson recap - Building classes and methods

6. Python Functions and Classes - Modules and Advanced Usage

31 - Lesson introduction - Modules and advanced usages
32 - Introduction to Python modules
33 - Working with imports
34 - Working with Python scripts
35 - Virtual environments and dependencies
36 - Lesson recap - Modules and advanced usages

7. Testing in Python - Introduction to Testing

37 - Lesson introduction - Writing and executing tests
38 - Motivations for testing in Python
39 - Testing conventions
40 - Testing with pytest
41 - Lesson recap - Writing and executing tests

8. Testing in Python - Writing Useful Tests

42 - Lesson introduction - Writing useful tests
43 - Using plan asserts in pytest
44 - Writing test classes
45 - Test classes vs. test functions
46 - Parameterizing tests
47 - Lesson recap - Writing useful tests

9. Testing in Python - Testing Failures

48 - Lesson introduction - Testing failures
49 - Test failure output
50 - Python debugging with PDB
51 - Other pytest runner options
52 - pytest fixtures
53 - Lesson recap - Testing failures

10. Introduction to pandas and NumPy - Basic pandas Usage

54 - Lesson introduction - Basic pandas usage
55 - Introduction to pandas
56 - Loading data into pandas
57 - Writing data from pandas DataFrames
58 - Exploratory analysis with pandas
59 - Lesson recap - Basic pandas usage

11. Introduction to pandas and NumPy - Working with DataFrames

60 - Lesson introduction - Working with DataFrames
61 - Common DataFrame operations
62 - Manipulating text in DataFrames
63 - Applying functions with pandas
64 - Visualizing data with pandas
65 - Lesson recap - Working with DataFrames

12. Introduction to pandas and NumPy - NumPy Basics

66 - Lesson introduction - NumPy basics
67 - Introduction to NumPy arrays
68 - Common NumPy array operations
69 - More NumPy array operations
70 - Lesson recap - NumPy basics

13. Applied Python for MLOps - Working with APIs and SDKs

71 - Lesson introduction - APIs and SDKs
72 - Installing the Azure command-line interface (CLI)
73 - Azure ML Studio with Python
74 - Hugging Face transformers
75 - Hugging Face datasets
76 - Azure open datasets
77 - Lesson recap - APIs and SDKs

14. Applied Python for MLOps - Automation with Command-Line Tools

78 - Lesson introduction - Automation with command-line tools
79 - Creating a single file script
80 - Using the argparse framework
81 - Declaring dependencies
82 - Using the Click framework
83 - Packaging your project
84 - Solving a machine learning problem with a CLI tool
85 - Lesson recap - Automation with command-line tools

15. Applied Python for MLOps - Building Machine Learning APIs

86 - Lesson introduction - Building machine learning APIs
87 - Introduction to the Flask framework
88 - Building an API with Flask
89 - Introduction to the FastAPI framework
90 - Building an API with FastAPI
91 - Python API best practices
92 - Lesson recap - Building machine learning APIs