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README.md
README.md
 
 
adding-data-dictionaries.ipynb
adding-data-dictionaries.ipynb
 
 
adding-data-lists.ipynb
adding-data-lists.ipynb
 
 
adding-methods.ipynb
adding-methods.ipynb
 
 
app.py
app.py
 
 
azureML_studio_with_python.ipynb
azureML_studio_with_python.ipynb
 
 
azure_opendatasets.ipynb
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class-inheritance.ipynb
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common-array-operations.ipynb
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conditionals-and-evaluations.ipynb
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dataset.html
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decorators.ipynb
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exceptions.ipynb
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exploratory-analysis.ipynb
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extracting-data-dictionaries.ipynb
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extracting-data-lists.ipynb
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function-arguments.ipynb
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function-basics.ipynb
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huggingface_dataset.ipynb
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introduction-dictionaries.ipynb
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introduction-to-arrays.ipynb
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introduction-to-classes.ipynb
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introduction-to-lists.ipynb
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introduction-to-modules.ipynb
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introduction-to-pandas.ipynb
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iterating-dictionaries.ipynb
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iterating-lists.ipynb
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jformat.py
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jformat2.py
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loading-data.ipynb
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non_test_examples.py
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other-datastructures.ipynb
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script.py
script.py
 
 
test_classes.py
test_classes.py
 
 
test_examples.py
test_examples.py
 
 
test_failure_output.py
test_failure_output.py
 
 
test_fixtures.py
test_fixtures.py
 
 
test_functions.py
test_functions.py
 
 
test_long_dictionaries.py
test_long_dictionaries.py
 
 
test_long_lists.py
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test_long_strings.py
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test_utils.py
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testing-conventions.ipynb
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testing-with-pytest.ipynb
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using-a-constructor.ipynb
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utils.py
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variables-and-assignments.ipynb
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wine-ratings.csv
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working-with-imports.ipynb
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working-with-types.ipynb
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world-championship-qualifier.csv
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writing-data.ipynb
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Python Essentials for MLOps

Overview

Welcome to the Python Essentials for MLOps project! This repository is designed to provide essential Python skills tailored for Machine Learning Operations (MLOps) professionals. Whether you're new to Python or looking to brush up on your skills specifically for MLOps tasks, this project aims to provide you with practical examples and exercises.

Project Goals

  • Provide a comprehensive guide to essential Python concepts relevant to MLOps.
  • Offer practical examples and exercises to reinforce learning.
  • Enable MLOps professionals to leverage Python effectively in their daily tasks such as data preprocessing, model training, deployment, and monitoring.

Topics Covered

  1. Python Basics: Covering fundamental Python syntax, data types, control structures, and functions.
  2. Data Handling: Exploring libraries like NumPy and Pandas for data manipulation and analysis.
  3. Data Visualization: Introducing Matplotlib and Seaborn for creating visualizations of data.
  4. Machine Learning with Python: Basics of machine learning using libraries like Scikit-learn.
  5. Model Deployment: Basics of deploying machine learning models using frameworks like Flask or FastAPI.
  6. Version Control: Introduction to Git for version control and collaboration on projects.
  7. Testing and Continuous Integration: Implementing testing strategies and setting up continuous integration workflows.
  8. Monitoring and Logging: Implementing logging and monitoring strategies for deployed models.

Getting Started

To get started with the Python Essentials for MLOps project, follow these steps:

  1. Clone this repository to your local machine.
  2. Navigate to the respective topic folders to access learning materials and exercises.
  3. Work through the examples and exercises, and feel free to experiment and modify the code to deepen your understanding.
  4. Join our community discussions or raise any questions or issues you encounter.

Contribution Guidelines

Contributions to this project are welcome! If you have suggestions for improvements, additional exercises, or new topics to cover, please feel free to open an issue or submit a pull request. Make sure to follow the contribution guidelines outlined in the CONTRIBUTING.md file.

License

This project is licensed under the MIT License.

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